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Bhardwaj, Vinayak, and Rincy Jacob. "Data Warehousing and OLAP Technology." Journal of Advance Research in Computer Science & Engineering (ISSN: 2456-3552) 1, no. 3 (March 31, 2014): 05–11. http://dx.doi.org/10.53555/nncse.v1i3.521.
Повний текст джерелаАнотація:
Data warehousing and Online Analytical Processing (OLAP) are essential elements of decision support, which has increasingly become a focus of the database industry. Data warehouse provides an effective way for the analysis and tatic to the mass data and helps to do the decision making. Many commercial products and services are now available and all of the principal database management system vendors now have offering in these areas. The paper introduces the data warehouse and online analysis process with an accent on their new requirements.
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Song, Yanan, and Xiaolong Hua. "Implementation of Data Mining Technology in Bonded Warehouse Inbound and Outbound Goods Trade." Journal of Organizational and End User Computing 34, no. 3 (May 2022): 1–18. http://dx.doi.org/10.4018/joeuc.291511.
Повний текст джерелаАнотація:
For the taxed goods, the actual freight is generally determined by multiplying the allocated freight for each KG and actual outgoing weight based on the outgoing order number on the outgoing bill. Considering the conventional logistics is insufficient to cope with the rapid response of e-commerce orders to logistics requirements, this work discussed the implementation of data mining technology in bonded warehouse inbound and outbound goods trade. Specifically, a bonded warehouse decision-making system with data warehouse, conceptual model, online analytical processing system, human-computer interaction module and WEB data sharing platform was developed. The statistical query module can be used to perform statistics and queries on warehousing operations. After the optimization of the whole warehousing business process, it only takes 19.1 hours to get the actual freight, which is nearly one third less than the time before optimization. This study could create a better environment for the development of China's processing trade.
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Salman, Ban, Nada M. Alhakkak, and Mustafa Musa Jaber. "Football Player Decision Support System Baghdad-City as a Case Study." International Journal of Engineering & Technology 7, no. 3.20 (September 1, 2018): 406. http://dx.doi.org/10.14419/ijet.v7i3.20.20582.
Повний текст джерелаАнотація:
Decision support system (DSS) is an area of information systems (IS) discipline which focuses on supporting decision-making. DSS includes personal decision support systems, executive information systems, online analytical processing systems, data warehousing, business intelligence, and group support systems. This paper introduced the implementation of a DSS related to football players with a case study.
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Fong, Joseph, Qing Li, and Shi-Ming Huang. "Universal Data Warehousing Based on a Meta-Data Modeling Approach." International Journal of Cooperative Information Systems 12, no. 03 (September 2003): 325–63. http://dx.doi.org/10.1142/s0218843003000772.
Повний текст джерелаАнотація:
Data warehouse contains vast amount of data to support complex queries of various Decision Support Systems (DSSs). It needs to store materialized views of data, which must be available consistently and instantaneously. Using a frame metadata model, this paper presents an architecture of a universal data warehousing with different data models. The frame metadata model represents the metadata of a data warehouse, which structures an application domain into classes, and integrates schemas of heterogeneous databases by capturing their semantics. A star schema is derived from user requirements based on the integrated schema, catalogued in the metadata, which stores the schema of relational database (RDB) and object-oriented database (OODB). Data materialization between RDB and OODB is achieved by unloading source database into sequential file and reloading into target database, through which an object relational view can be defined so as to allow the users to obtain the same warehouse view in different data models simultaneously. We describe our procedures of building the relational view of star schema by multidimensional SQL query, and the object oriented view of the data warehouse by Online Analytical Processing (OLAP) through method call, derived from the integrated schema. To validate our work, an application prototype system has been developed in a product sales data warehousing domain based on this approach.
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Samsinar, Riza, Jatmiko Endro Suseno, and Catur Edi Widodo. "Power Distribution Analysis For Electrical Usage In Province Area Using Olap (Online Analytical Processing)." E3S Web of Conferences 31 (2018): 11010. http://dx.doi.org/10.1051/e3sconf/20183111010.
Повний текст джерелаАнотація:
The distribution network is the closest power grid to the customer Electric service providers such as PT. PLN. The dispatching center of power grid companies is also the data center of the power grid where gathers great amount of operating information. The valuable information contained in these data means a lot for power grid operating management. The technique of data warehousing online analytical processing has been used to manage and analysis the great capacity of data. Specific methods for online analytics information systems resulting from data warehouse processing with OLAP are chart and query reporting. The information in the form of chart reporting consists of the load distribution chart based on the repetition of time, distribution chart on the area, the substation region chart and the electric load usage chart. The results of the OLAP process show the development of electric load distribution, as well as the analysis of information on the load of electric power consumption and become an alternative in presenting information related to peak load.
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Bhukya, Raghuram. "Generalization Driven Fuzzy Classification Rules Extraction using OLAM Data Cubes." International Journal of Engineering and Computer Science 9, no. 2 (February 28, 2020): 24962–69. http://dx.doi.org/10.18535/ijecs/v9i2.4444.
Повний текст джерелаАнотація:
An fuzzy classification rules extraction model for online analytical mining (OLAM) was explained in this article. The efficient integration of the concept of data warehousing, online analytical processing (OLAP) and data mining systems converges to OLAM results in an efficient decision support system. Even after associative classification proved as most efficient classification technique there is a lack of associative classification proposals in field of OLAM. While most of existing data cube models claims their superiority over other the fuzzy multidimensional data cubes proved to be more intuitive in user perspective and effectively manage data imprecision. Considering these factors, in this paper we propose an associative classification model which can perform classification over fuzzy data cubes. Our method aimed to improve accuracy and intuitive ness of classification model using fuzzy concepts and hierarchical relations. We also proposed a generalization-based criterion for ranking associative classification rules to improve classifier accuracy. The model accuracy tested on UCI standard database.
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Camilovic, Dragana, Dragana Becejski-Vujaklija, and Natasa Gospic. "A call detail records data mart: Data modeling and OLAP analysis." Computer Science and Information Systems 6, no. 2 (2009): 87–110. http://dx.doi.org/10.2298/csis0902087c.
Повний текст джерелаАнотація:
In order to succeed in the market, telecommunications companies are not competing solely on price. They have to expand their services based on their knowledge of customers' needs gained through the use of call detail records (CDR) and customer demographics. All the data should be stored together in the CDR data mart. The paper covers the topic of its design and development in detail and especially focuses on the conceptual/logical/physical trilogy. Some other design problems are also discussed. An important area is the problem involving time. This is why the implication of time in data warehousing is carefully considered. The CDR data mart provides the platform for Online Analytical Processing (OLAP) analysis. As it is presented in this paper, an OLAP system can help the telecommunications company to get better insight into its customers' behavior and improve its marketing campaigns and pricing strategies.
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Liu, Dun Nan, Xin Fan Jiang, and Si Yuan Zhang. "Operating Analysis and Data Mining System for Power Grid Dispatching." Advanced Materials Research 787 (September 2013): 611–17. http://dx.doi.org/10.4028/www.scientific.net/amr.787.611.
Повний текст джерелаАнотація:
The dispatching center of power-grid companies is also the data center of the power grid where gathers great amount of operating information. The valuable information contained in these data means a lot for power grid operating management, but at present there is no special method for the management of operating data resource. This paper introduces the operating analysis and data mining system for power grid dispatching. The technique of data warehousing online analytical processing has been used to manage and analysis the great capacity of data. This analysis system is based on the real-time data of the power grid, to dig out the potential rule of the power grid operating. This system also provides a research platform for the dispatchers, help to improve the JIT (Just in Time) management of power system.
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Oukid, Lamia, Omar Boussaid, Nadjia Benblidia, and Fadila Bentayeb. "TLabel." International Journal of Data Warehousing and Mining 12, no. 4 (October 2016): 54–74. http://dx.doi.org/10.4018/ijdwm.2016100103.
Повний текст джерелаАнотація:
Data Warehousing technologies and On-Line Analytical Processing (OLAP) feature a wide range of techniques for the analysis of structured data. However, these techniques are inadequate when it comes to analyzing textual data. Indeed, classical aggregation operators have earned their spurs in the online analysis of numerical data, but are unsuitable for the analysis of textual data. To alleviate this shortcoming, on-line analytical processing in text cubes requires new analysis operators adapted to textual data. In this paper, the authors propose a new aggregation operator named Text Label (TLabel), based on text categorization. Their operator aggregates textual data in several classes of documents. Each class is associated with a label that represents the semantic content of the textual data of the class. TLabel is founded on a tailoring of text mining techniques to OLAP. To validate their operator, the authors perform an experimental study and the preliminary results show the interest of their approach for Text OLAP.
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Venkatakrishnan, Ramesh. "Design, Implementation, and Assessment of Innovative Data Warehousing; Extract, Transformation, and Load(ETL); and Online Analytical Processing(OLAP) on BI." International Journal of Database Management Systems 12, no. 3 (June 30, 2020): 1–6. http://dx.doi.org/10.5121/ijdms.2020.12301.
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Rudikova, L. V. "ABOUT THE GENERAL CONCEPT OF THE UNIVERSAL STORAGE SYSTEM AND PRACTICE-ORIENTED DATA PROCESSING." «System analysis and applied information science», no. 2 (August 7, 2017): 12–19. http://dx.doi.org/10.21122/2309-4923-2017-2-12-19.
Повний текст джерелаАнотація:
Approaches evolution and concept of data accumulation in warehouse and subsequent Data Mining use is perspective due to the fact that, Belarusian segment of the same IT-developments is organizing. The article describes the general concept for creation a system of storage and practice-oriented data analysis, based on the data warehousing technology. The main aspect in universal system design on storage layer and working with data is approach uses extended data warehouse, based on universal platform of stored data, which grants access to storage and subsequent data analysis different structure and subject domains have compound’s points (nodes) and extended functional with data structure choice option for data storage and subsequent intrasystem integration. Describe the universal system general architecture of storage and analysis practice-oriented data, structural elements. Main components of universal system for storage and processing practice-oriented data are: online data sources, ETL-process, data warehouse, subsystem of analysis, users. An important place in the system is analytical processing of data, information search, document’s storage and providing a software interface for accessing the functionality of the system from the outside. An universal system based on describing concept will allow collection information of different subject domains, get analytical summaries, do data processing and apply appropriate Data Mining methods and algorithms.
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Wisnubhadra, Irya, Safiza Kamal Baharin, Nurul A. Emran, and Djoko Budiyanto Setyohadi. "QB4MobOLAP: A Vocabulary Extension for Mobility OLAP on the Semantic Web." Algorithms 14, no. 9 (September 13, 2021): 265. http://dx.doi.org/10.3390/a14090265.
Повний текст джерелаАнотація:
The accessibility of devices that track the positions of moving objects has attracted many researchers in Mobility Online Analytical Processing (Mobility OLAP). Mobility OLAP makes use of trajectory data warehousing techniques, which typically include a path of moving objects at a particular point in time. The Semantic Web (SW) users have published a large number of moving object datasets that include spatial and non-spatial data. These data are available as open data and require advanced analysis to aid in decision making. However, current SW technologies support advanced analysis only for multidimensional data warehouses and Online Analytical Processing (OLAP) over static spatial and non-spatial SW data. The existing technology does not support the modeling of moving object facts, the creation of basic mobility analytical queries, or the definition of fundamental operators and functions for moving object types. This article introduces the QB4MobOLAP vocabulary, which enables the analysis of mobility data stored in RDF cubes. This article defines Mobility OLAP operators and SPARQL user-defined functions. As a result, QB4MobOLAP vocabulary and the Mobility OLAP operators are evaluated by applying them to a practical use case of transportation analysis involving 8826 triples consisting of approximately 7000 fact triples. Each triple contains nearly 1000 temporal data points (equivalent to 7 million records in conventional databases). The execution of six pertinent spatiotemporal analytics query samples results in a practical, simple model with expressive performance for the enabling of executive decisions on transportation analysis.
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Sasikala, Mrs M., Ms D. Deepika, and Mr S. Shiva Shankar. "Pattern Identification and Predictions in Data Analysis." International Journal Of Engineering And Computer Science 7, no. 03 (March 5, 2018): 23686–91. http://dx.doi.org/10.18535/ijecs/v7i3.05.
Повний текст джерелаАнотація:
Data Mining is an analytic process to explore data in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new sets of data. The main target of data mining application is prediction. Predictive data mining is important and it has the most direct business applications in world. The paper briefly explains the process of data mining which consists of three stages: (1) the Initial exploration, (2) Pattern identification with validation, and (3) Deployment (application of the model to new data in order to generate predictions). Data Mining is being done for Patterns and Relationships recognitions in Data analysis, with an emphasis on large Observational data bases. From a statistical perspective Data Mining is viewed as computer automated exploratory data analytical system for large sets of data and it has huge Research challenges in India and abroad as well. Machine learning methods form the core of Data Mining and Decision tree learning. Data mining work is integrated within an existing user environment, including the works that already make use of data warehousing and Online Analytical Processing (OLAP). The paper describes how data mining tools predict future trends and behavior which allows in making proactive knowledge-driven decisions.
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Rodríguez-Mazahua, Nidia, Lisbeth Rodríguez-Mazahua, Asdrúbal López-Chau, Giner Alor-Hernández, and Isaac Machorro-Cano. "Decision-Tree-Based Horizontal Fragmentation Method for Data Warehouses." Applied Sciences 12, no. 21 (October 28, 2022): 10942. http://dx.doi.org/10.3390/app122110942.
Повний текст джерелаАнотація:
Data warehousing gives frameworks and means for enterprise administrators to methodically prepare, comprehend, and utilize the data to improve strategic decision-making skills. One of the principal challenges to data warehouse designers is fragmentation. Currently, several fragmentation approaches for data warehouses have been developed since this technique can decrease the OLAP (online analytical processing) query response time and it provides considerable benefits in table loading and maintenance tasks. In this paper, a horizontal fragmentation method, called FTree, that uses decision trees to fragment data warehouses is presented to take advantage of the effectiveness that this technique provides in classification. FTree determines the OLAP queries with major relevance, evaluates the predicates found in the workload, and according to this, builds the decision tree to select the horizontal fragmentation scheme. To verify that the design is correct, the SSB (star schema benchmark) was used in the first instance; later, a tourist data warehouse was built, and the fragmentation method was tested on it. The results of the experiments proved the efficacy of the method.
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Vaisman, Alejandro, and Esteban Zimányi. "Mobility Data Warehouses." ISPRS International Journal of Geo-Information 8, no. 4 (April 2, 2019): 170. http://dx.doi.org/10.3390/ijgi8040170.
Повний текст джерелаАнотація:
The interest in mobility data analysis has grown dramatically with the wide availability of devices that track the position of moving objects. Mobility analysis can be applied, for example, to analyze traffic flows. To support mobility analysis, trajectory data warehousing techniques can be used. Trajectory data warehouses typically include, as measures, segments of trajectories, linked to spatial and non-spatial contextual dimensions. This paper goes beyond this concept, by including, as measures, the trajectories of moving objects at any point in time. In this way, online analytical processing (OLAP) queries, typically including aggregation, can be combined with moving object queries, to express queries like “List the total number of trucks running at less than 2 km from each other more than 50% of its route in the province of Antwerp” in a concise and elegant way. Existing proposals for trajectory data warehouses do not support queries like this, since they are based on either the segmentation of the trajectories, or a pre-aggregation of measures. The solution presented here is implemented using MobilityDB, a moving object database that extends the PostgresSQL database with temporal data types, allowing seamless integration with relational spatial and non-spatial data. This integration leads to the concept of mobility data warehouses. This paper discusses modeling and querying mobility data warehouses, providing a comprehensive collection of queries implemented using PostgresSQL and PostGIS as database backend, extended with the libraries provided by MobilityDB.
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Schuetz, C. G., B. Neumayr, M. Schrefl, E. Gringinger, and S. Wilson. "Semantics-based summarisation of ATM information." Aeronautical Journal 123, no. 1268 (September 3, 2019): 1639–65. http://dx.doi.org/10.1017/aer.2019.74.
Повний текст джерелаАнотація:
ABSTRACTPilot briefings, in their traditional form, drown pilots in a sea of information. Rather than unfocused swathes of air traffic management (ATM) information, pilots require only the information for their specific flight, preferably with an emphasis on the most important information. In this paper, we introduce the notion of ATM information cubes – in analogy to the well-established concept of Online analytical processing (OLAP) cubes in data warehousing. We propose a framework with merge and abstraction operations for the combination and summarization of the information in ATM information cubes to obtain management summaries of relevant information. To this end, we adopt the concept of semantic data container – a package of data items with a semantic description of the contents. The semantic descriptions then serve to hierarchically organise semantic containers along the dimensions of an ATM information cube. Leveraging this hierarchical organisation, a merge operation combines ATM information from individual semantic containers and collects the data items into composite containers. An abstraction operation summarises the data items within a semantic container, replacing individual data items with more abstract data items with summary information.
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Dwi febryanto, Indra, Rahmat Berlianto, and Prihono Prihono. "Application of the Analytical Hierarchy Process (AHP) Method in Selecting Warehouse Locations for Onlineshop Goods Storage (Case Study: Expedited Shipment of Finished Goods)." PROZIMA (Productivity, Optimization and Manufacturing System Engineering) 6, no. 2 (January 31, 2023): 120–29. http://dx.doi.org/10.21070/prozima.v6i2.1578.
Повний текст джерелаАнотація:
Storage of finished goods is one of the important factors needed by a company, both offline and online manufacturing. The problem that often occurs is the lack of area and inadequate warehouse locations for storing finished goods. This problem is also found in the activities of storing finished goods in shipping expeditions. The condition of the warehouse which was initially less efficient was due to the location of the warehouse being less spacious, the absence of safety equipment and also areas prone to flooding. The goal to be achieved is to choose the optimal warehouse location from several existing alternatives. The method used is the Analytical Hierarchy Process (AHP) method to develop a hierarchy of logistics center location selection. Research variables using criteria include distance, cost, facilities, geographical position and warehouse area. The results of data processing produce the highest criterion weight value of 0.3228, namely the area of the warehouse and the smallest criterion weight of 0.0417, namely the cost criterion. In addition, the highest value of the work alternative is 0.4085 on the SPILL warehousing alternative and the smallest value is 0.2749 on the Rungkut Industri alternative.
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V, GUNASEKAR. "A STUDY ON IMPROVING SUPPLY CHAIN USING BUSINESS INTELLIGENCE CONCERNING DHARA LOGISTICS." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (May 2, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem32750.
Повний текст джерелаАнотація:
Business intelligence (BI) is software that ingests business data and presents it in user-friendly views such as reports, dashboards, charts and graphs. BI tools enable business users to access different types of data — historical and current, third-party and in-house, as well as semi-structured data and unstructured data like social media. Users can analyze this information to gain insights into how the business is performing. According to CIO magazine: “Although business intelligence does not tell business users what to do or what will happen if they take a certain course, neither is BI only about generating reports. Rather, BI offers a way for people to examine data to understand trends and derive insights.” Organizations can use the insights gained from business intelligence and data analysis to improve business decisions, identify problems or issues, spot market trends, and find new revenue or business opportunities. BI platforms traditionally rely on data warehouses for their baseline information. A data warehouse aggregates data from multiple data sources into one central system to support business analytics and reporting. Business intelligence software queries the warehouse and presents the results to the user in the form of reports, charts and maps. Data warehouses can include an online analytical processing (OLAP) engine to support multidimensional queries. “OLAP provides powerful technology for data discovery, facilitating business intelligence, complex analytic calculations and predictive analytics,” says IBM offering manager Doug Dailey in his data warehousing blog. “One of the main benefits of OLAP is the consistency of information and calculations it uses to drive data to improve product quality, customer interactions and process improvements.” Some newer business intelligence solutions can extract and ingest raw data directly using technology such as Hadoop, but data warehouses are still the data source of choice in many cases.
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Irtaimeh, Hani J., Abdallah Mishael Obeidat, Shadi H. Abualloush, and Amineh A. Khaddam. "Impact of Business Intelligence on Technical Creativity: A Case Study on AlHekma Pharmaceutical Company." European Scientific Journal, ESJ 12, no. 28 (October 31, 2016): 502. http://dx.doi.org/10.19044/esj.2016.v12n28p502.
Повний текст джерелаАнотація:
Business Intelligence, through its dimensions (data warehousing, data mining, direct analytical processing), helps the members of an organization to perceive and interpret their role in the organization’s creativity. For this reason, we may assume that Business Intelligence has an impact on Technical Creativity, and that matching of Business Intelligence and Technical Creativity will improve and achieve excellence in an organization. The aim of this study is to explore the impact of business intelligence dimensions (data warehousing, data mining, direct analytical processing) on Technical Creativity in AlHekma Pharmaceutical Company as a case study. For this purpose, a questionnaire was developed to collect data from the study population which consists of 50 employees. This is aimed at testing the hypotheses and achieving the objectives of the study. The most important results that the study achieved were that there was a statistically significant impact of business intelligence with its dimensions (data warehousing, data mining, and direct analytical processing) in technical creativity. The most important recommendations of the study were the necessity of organizations dependence on modern technology in order to develop their works. Thus, this is because this technology is recognized by its high accuracy on a completion of the work, as well as deepening the concept of technical creativity which gives them a competitive advantage in the marke
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Sharma, Pitambar, and Piyush Girdhar. "Online Analytical Processing (OLAP)." Journal of Advance Research in Computer Science & Engineering (ISSN: 2456-3552) 1, no. 3 (March 31, 2014): 01–04. http://dx.doi.org/10.53555/nncse.v1i3.520.
Повний текст джерелаАнотація:
This paper is basically accustomed define On-Line Analytical method (OLAP), WHO uses it and why, and to review the key choices required for OLAP code. On-Line Analytical method (OLAP) could also be a category of code technology that allows analysts, managers and executives to appreciate insight into info through fast, consistent, interactive access to an honest reasonably gettable views of {data of information} that has been transformed from data to mirror spatiality of the enterprise as understood by the user. whereas OLAP systems have the ability to answer "who?" and "what?" queries, it's their ability to answer "what if?" and "why?" that sets them except info Warehouses. OLAP applications span a variety of structure functions. Finance departments use OLAP for applications like budgeting, activity-based accountancy (allocations), cash performance analysis, and cash modelling. Sales analysis and prognostication square measure a pair of the OLAP applications found in sales departments. Among totally different applications, promoting departments use OLAP for analysis, sales prognostication, promotions analysis, consumer analysis, and market/customer segmentation. Typical manufacturing OLAP applications embody production coming up with and defect analysis. Although OLAP applications square measure found in wide divergent sensible areas, all of them want the following key choices like 3-D views of information, Calculation –intensive capabilities and Time Intelligence.
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Budianto, Galih. "Data Warehouse Modeling Using Online Analytical Processing Approach." Jurnal Ilmiah Informatika dan Ilmu Komputer (JIMA-ILKOM) 1, no. 1 (March 22, 2022): 7–13. http://dx.doi.org/10.58602/jima-ilkom.v1i1.2.
Повний текст джерелаАнотація:
Increasing business needs affect business competition in many companies that utilize information technology. The competition aims to gain advantages that utilize information technology in facilitating business processes. One of the conveniences offered is the use of information technology to support decision making in carrying out existing business processes in middle-to-upper scale companies that have large amounts of data. Online Analysis Processing (OLAP) is an approach method to present answers to the demand for a dimensional analysis process quickly, namely the design of applications and technologies that can collect, store, manipulate multidimensional data for analyst purposes. Information delivery generated from Online Analysis Processing (OLAP) can help executive information systems in generating sales reports because it has a response time of 0.0038769999519 seconds.
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Chou, Chung-Hsien, Masahiro Hayakawa, Atsushi Kitazawa, and Phillip Sheu. "GOLAP: Graph-Based Online Analytical Processing." International Journal of Semantic Computing 12, no. 04 (December 2018): 595–608. http://dx.doi.org/10.1142/s1793351x18500071.
Повний текст джерелаАнотація:
Graph-based Online Analytical Processing (GOLAP) extends Online Analytical Processing (OLAP) to address graph-based problems that involve object attributes. Based on graph data, GOLAP can answer user queries related to combinatorial optimization, structural analytics, and influence analytics. Besides, since a GOLAP system is an online interactive system that requires fast response time, the execution time for graph-problem queries is essentially critical. Thus, how to speed up the execution time of specific graph problems becomes a challenge in GOLAP. In this paper, we show several methods to speed up the running time, including graph data reduction and approximation. In this paper, we survey classes of graph-based queries, challenges for GOLAP, and solutions that GOLAP provides.
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Wijaya, Andri, Mutia Maharani, and Meilinda. "IMPLEMENTASI PENDEKATAN AGILE UNTUK PENGEMBANGAN OLAP DATA PENJUALAN." ZONAsi: Jurnal Sistem Informasi 6, no. 1 (March 3, 2024): 222–31. http://dx.doi.org/10.31849/zn.v6i1.17337.
Повний текст джерелаАнотація:
Sales data in large quantities are difficult to process and report using Microsoft Excel, which takes a long time. The proposed solution is to use Online Analytical Processing, a method that allows for faster decision-making through multidimensional data manipulation. In developing Online Analytical Processing for sales data, an agile approach is applied. With Online Analytical Processing, access to and display of transactional data becomes more efficient, improving analysis quality and supporting management decisions. Research results show that the prototype accelerates sales reports with a response time of 0.0039 seconds. Online Analytical Processing also facilitates decision-making in seconds. User Acceptance Testing results show high software quality and performance (100%), with an overall evaluation of 86,67% categorized as “Very Good”. Keywords: Sales Data, Online Analytical Processing, User Acceptance Testing, Agile Development
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Kumar, Narander, Vishal Verma, and Vipin Saxena. "A Security Algorithm for Online Analytical Processing Data Cube." International Journal of Computer Applications 79, no. 14 (October 18, 2013): 7–10. http://dx.doi.org/10.5120/13807-1768.
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Patel, Jigna Ashish, and Priyanka Sharma. "Online Analytical Processing for Business Intelligence in Big Data." Big Data 8, no. 6 (December 1, 2020): 501–18. http://dx.doi.org/10.1089/big.2020.0045.
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Aldisa, Rima Tamara. "Penerapan Online Analytical Processing (OLAP) dalam Pengelolaan Data Karyawan." JURIKOM (Jurnal Riset Komputer) 9, no. 1 (February 25, 2022): 55. http://dx.doi.org/10.30865/jurikom.v9i1.3832.
Повний текст джерелаАнотація:
At this time many companies manage employee data manually from the process of recording employee data, employee absences which can result in data errors. Because of several things, it is necessary to have a system that can record every incoming data as well as a method that can control it so as not to experience excess or deficiency. The method used is the Online Analytical Processing (OLAP) method. The Online Analytical Processing (OLAP) method is an approach method to provide answers to requests for dimensional analysis processes quickly, the result to be achieved is to be able to design applications that are expected to provide convenience for companies to carry out the process of recording employee data, employee attendance to presentation. reports quickly, precisely and accurately
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Abril Fradel, Diego Orlando, and José Nelson Pérez Castillo. "Current data warehousing and OLAP technologies’ status applied to spatial databases." Ingeniería e Investigación 27, no. 1 (January 1, 2007): 58–67. http://dx.doi.org/10.15446/ing.investig.v27n1.14782.
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Organisations require their information on a timely, dynamic, friendly, centralised and easy-to-access basis for analysing it and taking correct decisions at the right time. Centralisation can be achieved with data warehouse technology. On-line analytical processing (OLAP) is used for analysis. Technologies using graphics and maps in data presentation can be exploited for an overall view of a company and helping to take better decisions. Geographic information systems (GIS) are useful for spatially locating information and representing it using maps. Data warehouses are generally implemented with a multidimensional data model to make OLAP analysis easier. A fundamental point in this model is the definition of measurements and dimensions; geography lies within such dimensions. Many researchers have concluded that the geographic dimension is another attribute for describing data in current analysis systems but without having an in-depth study of its spatial feature and without locating them on a map, like GIS does. Seen this way, interoperability is necessary between GIS and OLAP (called spatial OLAP or SOLAP) and several entities are currently researching this. This document summarises the current status of such research.
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Purwanto, Joko, and Renny Renny. "Perancangan Data Warehouse Rumah Sakit Berbasis Online Analytical Processing (OLAP)." Jurnal Teknologi Informasi dan Ilmu Komputer 8, no. 5 (October 21, 2021): 1077. http://dx.doi.org/10.25126/jtiik.2021854232.
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<p class="BodyCxSpFirst">Pemanfaatan teknologi informasi sangat penting bagi rumah sakit, karena berpengaruh pula terhadap kualitas pelayanan kesehatan yang secara manual diubah menjadi digital dengan menggunakan teknologi informasi.Dalam penelitian ini penulis menggunakan metodologi <em>Nine step</em> sebagai acuan dalam merancang suatu <em>data warehouse</em><em>,</em> untuk pemodelan menggunakan skema konstelasi fakta dengan 3 tabel fakta dan 11 tabel dimensi. Perbedaan penelitian ini dengan penelitian sebelumnya terletak pada sumber data yang diekstrak langsung dari <em>database</em> SIMRS yang digunakan rumah sakit, sehingga tidak ada ekstraksi data secara manual.Penelitian ini bertujuan untuk menghasilkan desain data warehouse berbasis Online Analytical Processing (OLAP) sebagai sarana penunjang kualitas pelayanan kesehatan rumah sakit. OLAP yang dihasilkan akan berupa desain data warehouse dengan berbagai dimensi yang akan menghasilkan tampilan informasi berupa Chart maupun Grafik sehingga informasinya mudah dibaca dan dipahami oleh berbagai pihak.</p><p class="BodyCxSpFirst"> </p><p class="BodyCxSpFirst"><em><strong>Abtract</strong></em></p><p class="BodyCxSpFirst"><em>The use of information technology is very important for hospitals, because it also affects the quality of health services, which manualy changed to digital using information technology. In this study, the authors used the Nine step methodology as a reference in designing a data warehouse for modeling using a fact constellation schema with 3 fact tables and 11 dimension tables. the different in this study from previous research is that the data source was taken directly from the SIMRS database used by the hospital, so there is no manual data extraction.</em><em>The aim of this research is to be able to produce a Data Warehouse design based on Online Analytical Processing (OLAP) as a means of supporting the quality of hospital health services. The resulting OLAP will be a data warehouse design with various dimensions will produce the displays information in the form of a graph or chart so that the information is easy to read and understand by various parties.</em></p><p class="BodyCxSpLast"><em> </em></p><p class="BodyCxSpFirst"><em><strong><br /></strong></em></p>
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Yu, Dongjin, Dengwei Xu, Dongjing Wang, and Zhiyong Ni. "Hierarchical Topic Modeling of Twitter Data for Online Analytical Processing." IEEE Access 7 (2019): 12373–85. http://dx.doi.org/10.1109/access.2019.2891902.
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Attasena, Varunya, Nouria Harbi, and Jérôme Darmont. "A Novel Multi-Secret Sharing Approach for Secure Data Warehousing and On-Line Analysis Processing in the Cloud." International Journal of Data Warehousing and Mining 11, no. 2 (April 2015): 22–43. http://dx.doi.org/10.4018/ijdwm.2015040102.
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Cloud computing helps reduce costs, increase business agility and deploy solutions with a high return on investment for many types of applications, including data warehouses and on-line analytical processing. However, storing and transferring sensitive data into the cloud raises legitimate security concerns. In this paper, the authors' propose a new multi-secret sharing approach for deploying data warehouses in the cloud and allowing on-line analysis processing, while enforcing data privacy, integrity and availability. The authors' first validate the relevance of their approach theoretically and then experimentally with both a simple random dataset and the Star Schema Benchmark. The authors also demonstrate its superiority to related methods.
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Liliana, Lydia, Henny Hartono, and Devi Yurisca Bernanda. "INTEGRASI DATA MINING DAN ONLINE ANALYTICAL PROCESSING (OLAP) PADA DATA PERFORMA SISWA." Jurnal Sisfokom (Sistem Informasi dan Komputer) 9, no. 3 (November 2, 2020): 400–406. http://dx.doi.org/10.32736/sisfokom.v9i3.1022.
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Pertumbuhan teknologi membawa dampak terhadap peningkatan data untuk digunakan bagi setiap orang. Akumulasi data tersebut telah menciptakan pola data yang semakin banyak, namun perolehan informasi dari data tersebut masih minim. Oleh karena itu, saat ini diperlukan suatu teknik analisa data dalam mencari pola dari kumpulan data tersebut, salah satunya adalah data mining. Data mining merupakan proses pencarian informasi baru dari kumpulan data yang besar untuk menemukan informasi baru sebagai bahan pertimbangan dalam pengambilan keputusan di berbagai bidang, seperti bidang pendidikan. Dalam bidang pendidikan, banyak menghasilkan berbagai macam data, seperti data performa siswa dalam persiapan mengikuti ujian. Data tersebut dapat dianalisis dengan menggunakan metode On-Line Analytical Processing (OLAP) untuk menemukan pola dari data performa siswa tersebut. Penelitian ini berfokus pada proses integrasi data mining yang terdiri dari association, clustering, classification dan forecasting dengan kombinasi metode On-Line Analytical Processing (OLAP) pada data performa siswa. Penulis juga menggunakan bantuan tools Power OLAP untuk membantu analisa metode data mining. Hasil dari penelitian ini adalah penemuan pola baru dalam proses identifikasi kelompok data tersebut, seperti informasi mengenai rata-rata hasil ujian siswa berdasarkan persiapan ujian yang dilakukan dalam bentuk grafik sebagai alat pemodelan dari data, sehingga pengetahuan baru tersebut dapat membantu pihak universitas/sekolah untuk melalukan klasifikasi mengenai tingkat kelulusan dan dapat menetukan strategi dalam meningkatkan kelulusan siswa pada tahun - tahun berikutnya.
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Angelya, Tasya, Abdul Rahman, and Iis Pradesan. "Perancangan Data Warehouse Online Analytical Processing (OLAP) Data Hasil Kerja PT. ABC." MDP Student Conference 2, no. 1 (April 10, 2023): 656–64. http://dx.doi.org/10.35957/mdp-sc.v2i1.4241.
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PT. ABC merupakan salah satu perusahaan jasa yang bergerak dibidang Hutan Tanaman Industri. Perusahaan ini memiliki banyak tim yang tersebar di semua lokasi kerja sehingga data yang masuk semakin banyak pula, data-data tersebut tentunya perlu disimpan, diolah, dan dianalisis untuk menghasilkan suatu informasi yang berguna bagi perusahaan, dan dilaporkan kepada manajer untuk mengetahui keadaan perusahaan pada periode waktu tertentu. Dengan demikian dibutuhkanlah Data Warehouse yang digunakan untuk mendukung data yang dapat dimanfaatkan sebagai sumber informasi ketika menganalisis data. Perancangan Data Warehouse ini menggunakan metode Nine Step Kimball dengan model diagram Star Schema. Dalam mendukung perancangan data warehouse maka dibutuhkan tools yaitu Pentaho data Integration dan Tableau. Dengan menggunakan tools tersebut dapat dibangun sebuah Data Warehouse hasil tim dengan mengumpulkan data-data hasil kerja meliputi jenis pekerjaan, lokasi kerja, tanggal, petak kerja dan nama tim kerja yang berasal dari Ms. Excel lalu dimasukan kedalam database MySQL. Hasil dari penelitian ini adalah sebuah dashboard Online Analytical Processing (OLAP) yang berasal dari database MySQL lalu diolah ke berbagai informasi yang butuhkan, sehingga membentuk sebuah dashboard yang memberikan informasi kepada pihak eksekutif.
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Tan, Joseph, and Fuchung Wang. "Non-Traditional Data Mining Applications in Taiwan National Health Insurance (NHI) Databases." International Journal of Healthcare Information Systems and Informatics 12, no. 4 (October 2017): 31–51. http://dx.doi.org/10.4018/ijhisi.2017100103.
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This study examines time-sensitive applications of data mining methods to facilitate claims review processing and provide policy information for insurance decision-making vis-à-vis the Taiwan National Health Insurance (NHI) databases. In order to obtain the best payment management, a hybrid mining (HM) approach, which has been grounded on the extant knowledge of data mining projects and health insurance domain knowledge, is proposed. Through the integration of data warehousing, online analytic processing, data mining techniques and traditional data analysis in the healthcare field, an easy-to-use decision support platform, which will assist in directing the health insurance decision-making process, is built. Drawing from lessons learned within a case study setting, results showed that not only is HM approach a reliable, powerful, and user-friendly platform for diversified payment decision support, but that it also has great relevance for the practice and acceptance of evidence-based medicine. Essentially, HM approach can provide a critical boost to health insurance decision support; hence, future researchers should develop and improve the approach combined with their own application systems.
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Nabibayova, Gulnara. "Expanding the intellectual capabilities of OLAP technology using neural networks." Problems of Information Society 15, no. 2 (July 2, 2024): 43–48. http://dx.doi.org/10.25045/jpis.v15.i2.05.
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The article highlights the main characteristics, features and structure of Online Analytical Processing systems based on the same technology that perform online analytical processing of data. This technology allows analysts to explore and navigate a multidimensional indicator structure called an online analytical processing cube (data cube). Indicators (measures) of data cube play an important role in the decision-making process. To solve certain problems, these measures often need to be classified or grouped. Moreover, empty measures are common in data cube. This fact negatively affects strategic decision making. Unfortunately, online analytical processing itself is not well suited for classifying, clustering, and predicting empty measures of data cube in the presence of large data. In this regard, today there is a need to use new technologies to solve such problems. Such technologies include neural networks. The article discusses the problem of integrating online analytical processing and a neural network, showing the possibilities and advantages of such integration. It mentions that in the case of big data, the integration of OLAP and neural networks is very effective in solving problems of classification, clustering and empty measure prediction of data cube. An architectural and technological model for the integration of online analytical processing and neural networks is presented.
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Saputra, Eko. "Permodelan Data Warehouse Untuk Penjualan Ban Menggunakan Online Analytical Processing (OLAP)." Jurnal Ilmiah Informatika dan Ilmu Komputer (JIMA-ILKOM) 2, no. 1 (March 22, 2023): 12–18. http://dx.doi.org/10.58602/jima-ilkom.v2i1.13.
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Data warehouse akan memungkinkan integrasi data dari berbagai macam aplikasi atau sistem yang dapat menjamin akses yang lebih cepat bagi manajemen untuk memperoleh informasi dan menganalisanya sebagai bahan informasi. Penggunaan Teknologi OLAP dapat memudahkan para stakeholder dalam mengambil keputusan. Tujuan penelitian ini adalah merancang data warehouse untuk transaksi penjualan agar mendukung proses analisa bagi para pihak eksekutif dalam pengambilan keputusan. Data warehouse Penjualan dirancangan dengan menggunakan Nine Step Methodology data warehouse sehingga menghasilkan desain data warehouse yang lebih baik dengan menggunakan permodelan star schema, sehingga proses OLAP untuk information delivery data penjualan menampilkan grafik penjualan secara cepat.
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Islam, Md Shoriful, and G. M. Faruk Ahmed. "Online Analytical Processing for the Application of Data Cubes in Business Data Visualization." International Journal of Computer Graphics 7, no. 2 (November 30, 2016): 1–12. http://dx.doi.org/10.14257/ijcg.2016.7.2.01.
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DEHNE, FRANK, and HAMIDREZA ZABOLI. "PARALLEL CONSTRUCTION OF DATA CUBES ON MULTI-CORE MULTI-DISK PLATFORMS." Parallel Processing Letters 23, no. 01 (March 2013): 1350002. http://dx.doi.org/10.1142/s0129626413500023.
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On-line Analytical Processing (OLAP) has become one of the most powerful and prominent technologies for knowledge discovery in VLDB (Very Large Database) environments. Central to the OLAP paradigm is the data cube, a multi dimensional hierarchy of aggregate values that provides a rich analytical model for decision support. Various sequential algorithms for the efficient generation of the data cube have appeared in the literature. However, given the size of contemporary data warehousing repositories, multi-processor solutions are crucial for the massive computational demands of current and future OLAP systems. In this paper we discuss the development of MCMD-CUBE, a new parallel data cube construction method for multi-core processors with multiple disks. We present experimental results for a Sandy Bridge multi-core processor with four parallel disks. Our experiments indicate that MCMD-CUBE achieves very close to linear speedup. A critical part of our MCMD-CUBE method is parallel sorting. We developed a new parallel sorting method termed MCMD-SORT for multi-core processors with multiple disks which outperforms other previous methods.
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Gowda, Harsha, Julijana Ivanisevic, Caroline H. Johnson, Michael E. Kurczy, H. Paul Benton, Duane Rinehart, Thomas Nguyen, et al. "Interactive XCMS Online: Simplifying Advanced Metabolomic Data Processing and Subsequent Statistical Analyses." Analytical Chemistry 86, no. 14 (June 25, 2014): 6931–39. http://dx.doi.org/10.1021/ac500734c.
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Afrianda, Rio, Veithzal Rivai Zainal, and Indra Siswanti. "Business Intelligence Strategy for Company Business Development Using Online Analytical Processing." Indikator: Jurnal Ilmiah Manajemen dan Bisnis 7, no. 3 (August 1, 2023): 51. http://dx.doi.org/10.22441/indikator.v7i3.19171.
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The purpose of this article is to discuss Business Intelligence and its role in increasing a company's competitive advantage through the utilization of various data, information and knowledge owned by a company as a raw material in the decision-making process. The method used in this article uses Online Analytical Processing. The results of the research are (1) data analysis has become a major and vital requirement in efforts to increase the business competitiveness of an organization or company; (2) entrepreneur-style decision making that tends to rely on intuition becomes less suitable in the midst of an increasingly competitive and complicated business environment; (3) BI is an e-business application that functions to convert data within the company (operational, transactional, and other data) into a form of knowledge; (4) BI emphasizes the implementation of the 5 utilization of information for the purposes of data sourcing, data analysis, situation awareness, risk analysis, and decision support.
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Dila Wulandari, Relensia Irda, and Maylina Destriani Br Milala. "ANALISIS SEKTOR UNGGULAN DI KOTA LANGSA." JURNAL ILMIAH EKONOMI DAN MANAJEMEN 1, no. 3 (November 26, 2023): 285–90. http://dx.doi.org/10.61722/jiem.v1i3.242.
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This research aims to identify the leading sectors in Kota Langsa as an effort to support regional economic growth. The analytical method employed involves the identification of leading sectors based on the Gross Regional Domestic Product (GRDP) data for the period 2018-2022. By using the location quotient and shift-share approaches, the research findings reveal that there are 12 leading sectors in Kota Langsa. These sectors include the processing industry, Water Supply, Waste Management, Recycling, Construction, Wholesale and Retail Trade; Repair of Motor Vehicles and Motorcycles, Transportation and Warehousing, Accommodation and Food Services, Information and Communication, Financial and Insurance Services, Real Estate, Company Services, Health and Social Activities, and finally, the Other Services sector..
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Shadlou, Saeed, Ng Jie Kai, and Abdolreza Hajmoosaei. "Online Payment via PayPal API Case Study Event Registration Management System (ERMS)." International Journal of Web Portals 3, no. 2 (April 2011): 30–37. http://dx.doi.org/10.4018/jwp.2011040104.
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PayPal is an international payment gateway allowing businesses and individuals to transfer funds in a secure manner over the Internet. Using PayPal to accept payments has several advantages for online merchants. It is a recognized brand when it comes to Business to Consumer (B2C) transactions, creating a business account with PayPal is easier and faster, and finally, PayPal lends its name to the transaction, so customers may feel more comfortable entering into a transaction with a previously unknown merchant. Besides the mentioned advantages, PayPal’s transaction dispute system requires a tracking number from a shipped package to respond to a customer dispute. If the product is purely electronic (a download or access to a site, for example), one’s response to disputes will be quite limited. The solution for the problem mentioned above is PayPal API. The PayPal API resolves Pay Pal drawback through maintaining card and bank account payment schedules without the liability of warehousing payment data also processing one-time and recurring payments. For the evaluation of Pay Pal API, the authors develop an Event Registration Management System (ERMS). ERMS serves as a platform for users to make registrations for events such as conferences, seminars, and workshops.
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Kim, Juhyun, and Changjoo Moon. "The Distributed HTAP Architecture for Real-Time Analysis and Updating of Point Cloud Data." Electronics 12, no. 18 (September 20, 2023): 3959. http://dx.doi.org/10.3390/electronics12183959.
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Updating the most recent set of point cloud data is critical in autonomous driving environments. However, existing systems for point cloud data management often fail to ensure real-time updates or encounter situations in which data cannot be effectively refreshed. To address these challenges, this study proposes a distributed hybrid transactional/analytical processing architecture designed for the efficient management and real-time processing of point cloud data. The proposed architecture leverages both columnar and row-based tables, enabling it to handle the substantial workloads associated with its hybrid architecture. The construction of this architecture as a distributed database cluster ensures real-time online analytical process query performance through query parallelization. A dissimilarity analysis algorithm for point cloud data, built by utilizing the capabilities of the spatial database, updates the point cloud data for the relevant area whenever the online analytical process query results indicate high dissimilarity. This research contributes to ensuring real-time hybrid transactional/analytical processing workload processing in dynamic road environments, helping autonomous vehicles generate safe, optimized routes.
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Sulianta, Feri, and Philothra Clarissa Raina. "Kelola Kubikal Data Transaksional Sistem Informasi Rumah Sakit Dengan Teknik Online Analytical Processing." MIND Journal 1, no. 1 (May 12, 2018): 1. http://dx.doi.org/10.26760/mindjournal.v1i1.1.
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Data transaksional rumah sakit dapat diberdayakan lebih lanjut untuk ragam keperluan dan bukan hanya sebagai arsip riwayat pasien perseorangan saja. Berbagai informasi berharga dapat diungkapkan dari data transkasional rumah sakit yang dihasilkan dari sistem rekam medis.Dalam kasus ini untuk mendapatkan kejelasan yang melibatkan informasi menyeluruh yang juga melibatkan ragam sudut pandang dapat disolusikan dengan teknik Online Analytical Processing (OLAP). Teknik ini mampu mengakomodasi kelengkapan data yang nantinya menjadi framework untuk dianalisa secara seksama Mengacu pada data rekam medis dimana setiap pasien memiliki banyak keluhan dan latar belakang yang berbeda yang terelasi dengan sang pasien. Teknik OLAP mampu menyajikan data dalam bentuk multidimensi. Selanjutnya, OLAP akan melakukan eksekusi data yakni slicing(irisan) dan dicing(rotasi) yakni meringkas dan mengumpulkan sejumlah besar data, melakukan filtering, pengurutan, dan memberikan peringkat (rangking) yang akan memperkaya temuan berharga dari data kubikal.
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Darman, Ridho. "ANALISIS DATA KEJADIAN BENCANA ANGIN PUTING BELIUNG DENGAN METODE ONLINE ANALYTICAL PROCESSING (OLAP)." SINTECH (Science and Information Technology) Journal 2, no. 1 (April 21, 2019): 18–23. http://dx.doi.org/10.31598/sintechjournal.v2i1.298.
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A whirlwind is a natural disaster with a relatively high incidence. In improving whirlwinddisaster mitigation preparedness, analysis of historical data of events is needed to minimize the possibility of losses. In this study, data analysis was carried out using the Online Analytical Processing (OLAP) method with the Zoho Reports application so that it can be known to the region prone to whirlwind and the time of occurrence to help those who have an importance in decision making. The results of the analysis are in the form of information displayed in graphical form from data on the occurrence of whirlwind in Indonesia in 2011-2014.
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Dandy, Dandy, and Rino Rino. "Implementation of Business Intelligence in Data Superstore Sales with Online Analytical Processing Method." bit-Tech 3, no. 2 (April 18, 2021): 44–50. http://dx.doi.org/10.32877/bt.v3i2.182.
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Transaction data in superstore sales data are very useful for company development, can be used to describe and forecast or predict future sales transaction data and to study the past about business opportunities and challenges. The use of Business Intelligence (BI) technology can help analyze large amounts of data, in addition, BI is a powerful tool for quality analysis and company analysis. This study designed an information system using the BI approach to analyze transaction data on superstore sales data. The research focus is on report data, namely superstore sales data regarding sales transactions. This study uses the OLAP method to describe data visualization so that it provides benefits and competitive advantages. This system can improve the quality of decisions taken in solving the problem of abundant data accumulation, monitoring operational activities, fulfilling information needs and effective data management. Business intelligence is expected for company leaders to be able to understand the data that will have been processed in understanding visual forms and can easily absorb the information needed to make decisions for the company. In addition, with the design of website-based business intelligence that is effective and efficient to produce opportunities in making decisions to predict the increase or decrease that will occur in the coming years using the histories in the superstore sales data of the previous years
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Qahhariana, Anna, and Imas Sukaesih Sitanggang. "Peningkatan Kinerja Sistem Spatial Online Analytical Processing (SOLAP) Titik Panas Kebakaran Hutan." Jurnal Ilmu Komputer dan Agri-Informatika 5, no. 1 (July 25, 2018): 21. http://dx.doi.org/10.29244/jika.5.1.21-30.
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Data histori titik panas sebagai salah satu indikator kebakaran hutan dan lahan dapat dikelola dengan teknologi data warehouse dan sistem spatial online analytical processing (SOLAP). Pada penelitian sebelumnya telah dilakukan peningkatan kinerja terhadap sistem tersebut sehingga titik panas yang mampu dihasilkan meningkat menjadi 1500 titik. Penelitian ini bertujuan untuk meningkatkan kinerja sistem SOLAP data titik panas yang telah dibangun dalam penelitian sebelumnya. Peningkatan kinerja meliputi konfigurasi dari sisi perangkat lunak seperti peningkatan Java runtime environment (JRE), peningkatan server Apache Tomcat, dan peringkasan proses Javascript object notation (JSON) sedangkan spesifikasi perangkat keras menggunakan spesifikasi RAM dan processor yang sama dengan penelitian sebelumnya. Jumlah titik panas hasil query yang mampu dihasilkan dari konfigurasi tersebut meningkat menjadi 5344 titik.<br /><br />Kata kunci: kebakaran hutan, spatial data warehouse, spatial OLAP, titik panas
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Huang, Yanxian. "Differences in Online Sales of Agricultural Products from the Perspective of Farmers." Frontiers in Business, Economics and Management 5, no. 3 (October 14, 2022): 154–60. http://dx.doi.org/10.54097/fbem.v5i3.1999.
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The development and popularization of network technology and the gradual rise of network sales of agricultural products not only provide a good opportunity for farmers engaged in the sale of agricultural products, but also play a role in promoting the development of agricultural products sales. In this paper, based on the micro-survey data of typical e-commerce village farmers, the differences in the effect of online sales of agricultural products on income increase are studied from the perspective of different types of agricultural products. The results show that the online sales of fresh and dry agricultural products can improve the household income of farmers, and the latter's income-increasing effect is stronger than the former, mainly because of the differences in characteristics of agricultural products, operating costs and value appreciation. In order to further improve the income-increasing effect of online sales of agricultural products, the government should continue to promote the construction of software and hardware infrastructure of online sales of agricultural products in rural areas, such as network, transportation, cold chain and warehousing, encourage the innovation of packaging technology, and create a good online sales environment for farmers in more areas. At the same time, efforts should be made to promote the branding, quality and value of agricultural products, guide farmers to produce high-quality agricultural products, and support competent farmers to carry out agricultural product processing.
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Martinez-Mosquera, Diana, Rosa Navarrete, Sergio Luján-Mora, Lorena Recalde, and Andres Andrade-Cabrera. "Integrating OLAP with NoSQL Databases in Big Data Environments: Systematic Mapping." Big Data and Cognitive Computing 8, no. 6 (June 5, 2024): 64. http://dx.doi.org/10.3390/bdcc8060064.
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The growing importance of data analytics is leading to a shift in data management strategy at many companies, moving away from simple data storage towards adopting Online Analytical Processing (OLAP) query analysis. Concurrently, NoSQL databases are gaining ground as the preferred choice for storing and querying analytical data. This article presents a comprehensive, systematic mapping, aiming to consolidate research efforts related to the integration of OLAP with NoSQL databases in Big Data environments. After identifying 1646 initial research studies from scientific digital repositories, a thorough examination of their content resulted in the acceptance of 22 studies. Utilizing the snowballing technique, an additional three studies were selected, culminating in a final corpus of twenty-five relevant articles. This review addresses the growing importance of leveraging NoSQL databases for OLAP query analysis in response to increasing data analytics demands. By identifying the most commonly used NoSQL databases with OLAP, such as column-oriented and document-oriented, prevalent OLAP modeling methods, such as Relational Online Analytical Processing (ROLAP) and Multidimensional Online Analytical Processing (MOLAP), and suggested models for batch and real-time processing, among other results, this research provides a roadmap for organizations navigating the integration of OLAP with NoSQL. Additionally, exploring computational resource requirements and performance benchmarks facilitates informed decision making and promotes advancements in Big Data analytics. The main findings of this review provide valuable insights and updated information regarding the integration of OLAP cubes with NoSQL databases to benefit future research, industry practitioners, and academia alike. This consolidation of research efforts not only promotes innovative solutions but also promises reduced operational costs compared to traditional database systems.
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Rudikova, L. V., and O. R. Myslivec. "About a concept of creating a social network users information aggregation and data processing system." «System analysis and applied information science», no. 4 (February 6, 2019): 65–72. http://dx.doi.org/10.21122/2309-4923-2018-4-65-72.
Повний текст джерелаАнотація:
The development of a general concept and implementation of a data-storage and analysis system for practice oriented data, one of the subsystems of which is an analytical system for the accumulation and analysis of data from users of social networks, is topical. The development of a general concept and implementation of a data-storage and analysis system for practice oriented data, one of the subsystems of which is an analytical system for the accumulation and analysis of data from users of social networks, is topical. Data that users leave about themselves in social networks can be useful in solving various tasks. The proposed article describes the subject area associated with the collection and storage of data from users of social networks. Proceeding from the subject area, the general architecture of the universal data collection and storage system is proposed, which is based on the client-server architecture. For the server side of the system, a fragment of the data model is provided, which is associated with the accumulation of data from external sources. The framework of the system architecture is described. The developed universal system is based on the information technology of data warehousing, and it has the following aspects: an expandable complex subject area, the integration of stored data that come from various sources, the invariance of stored data in time with mandatory labels, relatively high data stability, the search for necessary trade-off in data redundancy, modularity of individual system units, fl and extensibility of the architecture, high security requirements vulnerable data.The proposed system organizes the process of collecting data and filling the database from external sources. To do this, the system has a module for collecting and converting information from third-party Internet sources and sending them to the database. The system is intended for various users interested in analyzing data of users of social networks.
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Alonso-Secades, Vidal, Alfonso-José López-Rivero, Manuel Martín-Merino-Acera, Manuel-José Ruiz-García, and Olga Arranz-García. "Designing an Intelligent Virtual Educational System to Improve the Efficiency of Primary Education in Developing Countries." Electronics 11, no. 9 (May 6, 2022): 1487. http://dx.doi.org/10.3390/electronics11091487.
Повний текст джерелаАнотація:
Incorporating technology into virtual education encourages educational institutions to demand a migration from the current learning management system towards an intelligent virtual educational system, seeking greater benefit by exploiting the data generated by students in their day-to-day activities. Therefore, the design of these intelligent systems must be performed from a new perspective, which will take advantage of the new analytical functions provided by technologies such as artificial intelligence, big data, educational data mining techniques, and web analytics. This paper focuses on primary education in developing countries, showing the design of an intelligent virtual educational system to improve the efficiency of primary education through recommendations based on reliable data. The intelligent system is formed of four subsystems: data warehousing, analytical data processing, monitoring process and recommender system for educational agents. To illustrate this, the paper contains two dashboards that analyze, respectively, the digital resources usage time and an aggregate profile of teachers’ digital skills, in order to infer new activities that improve efficiency. These intelligent virtual educational systems focus the teaching–learning process on new forms of interaction on an educational future oriented to personalized teaching for the students, and new evaluation and teaching processes for each professor.