Relevant bibliographies by topics / Architecture analytics

Academic literature on the topic 'Architecture analytics'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Architecture analytics"

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Kumar, S. Senthil, and Ms V. Kirthika. "Big Data Analytics Architecture and Challenges, Issues of Big Data Analytics." International Journal of Trend in Scientific Research and Development Volume-1, Issue-6 (October 31, 2017): 669–73. http://dx.doi.org/10.31142/ijtsrd4673.

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Marah, Bockarie Daniel, Zilong Jing, Tinghuai Ma, Raeed Alsabri, Raphael Anaadumba, Abdullah Al-Dhelaan, and Mohammed Al-Dhelaan. "Smartphone Architecture for Edge-Centric IoT Analytics." Sensors 20, no. 3 (February 7, 2020): 892. http://dx.doi.org/10.3390/s20030892.

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The current baseline architectures in the field of the Internet of Things (IoT) strongly recommends the use of edge computing in the design of the solution applications instead of the traditional approach which solely uses the cloud/core for analysis and data storage. This research, therefore, focuses on formulating an edge-centric IoT architecture for smartphones which are very popular electronic devices that are capable of executing complex computational tasks at the network edge. A novel smartphone IoT architecture (SMIoT) is introduced that supports data capture and preprocessing, model (i.e., machine learning models) deployment, model evaluation and model updating tasks. Moreover, a novel model evaluation and updating scheme is provided which ensures model validation in real-time. This ensures a sustainable and reliable model at the network edge that automatically adjusts to changes in the IoT data subspace. Finally, the proposed architecture is tested and evaluated using an IoT use case.
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Hinojosa-Palafox, Eduardo A., Oscar M. Rodríguez-Elías, José A. Hoyo-Montaño, Jesús H. Pacheco-Ramírez, and José M. Nieto-Jalil. "An Analytics Environment Architecture for Industrial Cyber-Physical Systems Big Data Solutions." Sensors 21, no. 13 (June 23, 2021): 4282. http://dx.doi.org/10.3390/s21134282.

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The architecture design of industrial data analytics system addresses industrial process challenges and the design phase of the industrial Big Data management drivers that consider the novel paradigm in integrating Big Data technologies into industrial cyber-physical systems (iCPS). The goal of this paper is to support the design of analytics Big Data solutions for iCPS for the modeling of data elements, predictive analysis, inference of the key performance indicators, and real-time analytics, through the proposal of an architecture that will support the integration from IIoT environment, communications, and the cloud in the iCPS. An attribute driven design (ADD) approach has been adopted for architectural design gathering requirements from smart production planning, manufacturing process monitoring, and active preventive maintenance, repair, and overhaul (MRO) scenarios. Data management drivers presented consider new Big Data modeling analytics techniques that show data is an invaluable asset in iCPS. An architectural design reference for a Big Data analytics architecture is proposed. The before-mentioned architecture supports the Industrial Internet of Things (IIoT) environment, communications, and the cloud in the iCPS context. A fault diagnosis case study illustrates how the reference architecture is applied to meet the functional and quality requirements for Big Data analytics in iCPS.
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Barrad, Sherif, Stéphane Gagnon, and Raul Valverde. "An Analytics Architecture for Procurement." International Journal of Information Technologies and Systems Approach 13, no. 2 (July 2020): 73–98. http://dx.doi.org/10.4018/ijitsa.2020070104.

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Procurement transformation and pure cost reduction are no longer a novelty in today's modern business world. Procurement, as a core business function, plays a key role given its ability to generate value for the firm. From maximizing supplier value to minimizing contract leakage, challenges seldomly lack in this department. In fact, both resource and skill shortages and technology limitations are typically “top-of-mind” for Procurement Executives. Many research articles around the concept of cost reduction however, limited literature has been published in the areas of Artificial Intelligence, analytics and Rules-Based Systems and their specific application in Procurement. This article proposes a new enterprise architecture, leveraging emerging technologies to guide procurement organizations in their digital transformation. Our intent is to discuss how analytics, business rules and complex event processing (CEP) can be explored and adapted to the world of procurement to help reduce costs. This article concludes by suggesting an approach to implement the proposed architecture.
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Choudhury, Dwaipayan, Aravind Sukumaran Rajam, Ananth Kalyanaraman, and Partha Pratim Pande. "High-Performance and Energy-Efficient 3D Manycore GPU Architecture for Accelerating Graph Analytics." ACM Journal on Emerging Technologies in Computing Systems 18, no. 1 (January 31, 2022): 1–19. http://dx.doi.org/10.1145/3482880.

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Recent advances in GPU-based manycore accelerators provide the opportunity to efficiently process large-scale graphs on chip. However, real world graphs have a diverse range of topology and connectivity patterns (e.g., degree distributions) that make the design of input-agnostic hardware architectures a challenge. Network-on-Chip (NoC)- based architectures provide a way to overcome this challenge as the architectural topology can be used to approximately model the expected traffic patterns that emerge from graph application workloads. In this paper, we first study the mix of long- and short-range traffic patterns generated on-chip using graph workloads, and subsequently use the findings to adapt the design of an optimal NoC-based architecture. In particular, by leveraging emerging three-dimensional (3D) integration technology, we propose design of a small-world NoC (SWNoC)- enabled manycore GPU architecture, where the placement of the links connecting the streaming multiprocessors (SM) and the memory controllers (MC) follow a power-law distribution. The proposed 3D manycore GPU architecture outperforms the traditional planar (2D) counterparts in both performance and energy consumption. Moreover, by adopting a joint performance-thermal optimization strategy, we address the thermal concerns in a 3D design without noticeably compromising the achievable performance. The 3D integration technology is also leveraged to incorporate Near Data Processing (NDP) to complement the performance benefits introduced by the SWNoC architecture. As graph applications are inherently memory intensive, off-chip data movement gives rise to latency and energy overheads in the presence of external DRAM. In conventional GPU architectures, as the main memory layer is not integrated with the logic, off-chip data movement negatively impacts overall performance and energy consumption. We demonstrate that NDP significantly reduces the overheads associated with such frequent and irregular memory accesses in graph-based applications. The proposed SWNoC-enabled NDP framework that integrates 3D memory (like Micron's HMC) with a massive number of GPU cores achieves 29.5% performance improvement and 30.03% less energy consumption on average compared to a conventional planar Mesh-based design with external DRAM.
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Srinivas, Shreya, Asif Qumer Gill, and Terry Roach. "Analytics-Enabled Adaptive Business Architecture Modeling." Complex Systems Informatics and Modeling Quarterly, no. 23 (July 31, 2020): 23–43. http://dx.doi.org/10.7250/csimq.2020-23.03.

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., Nagalakshmi D. R. "REMOTE ONLINE BIG DATA ANALYTICS ARCHITECTURE." International Journal of Research in Engineering and Technology 05, no. 16 (May 25, 2016): 99–101. http://dx.doi.org/10.15623/ijret.2016.0516021.

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Esteban-Maluenda, Ana, Laura Sánchez Carrasco, and Luis San Pablo Moreno. "ArchiText Mining: Applying Text Analytics to Research on Modern Architecture." Život umjetnosti, no. 105 (December 31, 2019): 158–79. http://dx.doi.org/10.31664/zu.2019.105.07.

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ArchiteXt Mining: Spanish Modern Architecture through Its Texts (1939–1975) is a research project funded by the Government of Spain through the 2015 Call for “Excellence Projects” of the Ministry of Economy and Competitiveness. This project aims to explore a new viewpoint and look into the special features of Spanish modern architecture. Despite the increasing success of using data analysis as a tool in a variety of disciplines, research on architectural theory has never made the most efficient use of these technologies. The Spanish and international circumstances of modern architecture development have been scrutinized through qualitative research, which has established a shared theoretical ground. It is now time to start a new in-depth research based on objective data. To address this challenge, we propose the application of text mining techniques to take advantage of the best data source in the field: architectural periodicals. The purpose is to create a powerful database hosted on a public website for the scientific community. Thus, this project fulfils several e-Research objectives: to facilitate the computerization of data research, to support every stageof data collection, and to manage big data analyses with thehelp of specific tools.
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Amare, Meseret Yihun, and Stanislava Simonova. "Learning analytics for higher education: proposal of big data ingestion architecture." SHS Web of Conferences 92 (2021): 02002. http://dx.doi.org/10.1051/shsconf/20219202002.

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Research background: Higher education institutions are generating multiple formats of data from diverse sources across the globe. The data ingestion layer is responsible for collecting data and transform for analysis. Learning analytics plays a vital role in providing decision-making support and selection of suitable timely intervention. The lack of tailored big-data ingestion architectures for academics led to several implementation challenges. Purpose of the article: The purpose of this article is to propose data ingestion architecture enabled for big data learning analytics. Methods: The study reviews existing literature to examine big-data ingestion tools and frameworks; and identify big-data ingestion challenges. An optimized framework for the real world learning analytics application was not yet in place at global higher educations. Consequently, the big-data ingestion pipeline is experiencing challenges of inefficient and complex data access, slow processing time, and security issues associated with transferring data to the system. The proposed data ingestion architecture is based on review of recent literature and adapts best international practices, guidelines, and techniques to meet the demand of current big-data ingestion issues. Findings & value added: This study identifies the current global challenges in implementing learning analytics projects. Review of recent big data ingestion techniques has been done based on defined metrics tuned for learning analytics purposes. The proposed data ingestion framework would increase the effectiveness of collecting, importing, processing and storing of learning data. Besides, the proposed architecture contributes to the construction of full-fledged big-data learning analytics ecosystem of higher educations.
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Kenda, Klemen, Nikolaos Mellios, Matej Senožetnik, and Petra Pergar. "Computer Architectures for Incremental Learning in Water Management." Sustainability 14, no. 5 (March 2, 2022): 2886. http://dx.doi.org/10.3390/su14052886.

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This paper presents an architecture and a platform for processing of water management data in real time. Stakeholders in the domain are faced with the challenge of handling large amounts of incoming sensor data from heterogeneous sources after the digitalization efforts within the sector. Our water management analytical platform (WMAP) is built upon the needs of domain experts (it provides capabilities for offline analysis) and is designed to solve real-world problems (it provides real-time data flow solutions and data-driven predictive analytics) for smart water management. WMAP is expected to contribute significantly to the water management domain, which has not yet acquired the competences to implement extensive data analysis and modeling capabilities in real-world scenarios. The proposed architecture extends existing big data architectures and presents an efficient way of dealing with data-driven modeling in the water management domain. The main improvement is in the speed (online analytics) layer of the architecture, where we introduce heterogeneous data fusion in a set of data streams that provide real-time data-driven modeling and prediction services. Using the proposed architecture, the results illustrate that models built with datasets with richer contextual information and multiple data sources are more accurate and thus more useful.
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Dissertations / Theses on the topic "Architecture analytics"

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Ho, Quan. "Architecture and Applications of a Geovisual Analytics Framework." Doctoral thesis, Linköpings universitet, Medie- och Informationsteknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-91679.

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The large and ever-increasing amounts of multi-dimensional, multivariate, multi-source, spatio-temporal data represent a major challenge for the future. The need to analyse and make decisions based on these data streams, often in time-critical situations, demands integrated, automatic and sophisticated interactive tools that aid the user to manage, process, visualize and interact with large data spaces. The rise of `Web 2.0', which is undisputedly linked with developments such as blogs, wikis and social networking, and the internet usage explosion in the last decade represent another challenge for adapting these tools to the Internet to reach a broader user community. In this context, the research presented in this thesis introduces an effective web-enabled geovisual analytics framework implemented, applied and verified in Adobe Flash ActionScript and HTML5/JavaScript. It has been developed based on the principles behind Visual Analytics and designed to significantly reduce the time and effort needed to develop customized web-enabled applications for geovisual analytics tasks and to bring the benefits of visual analytics to the public. The framework has been developed based on a component architecture and includes a wide range of visualization techniques enhanced with various interaction techniques and interactive features to support better data exploration and analysis. The importance of multiple coordinated and linked views is emphasized and a number of effective techniques for linking views are introduced. Research has so far focused more on tools that explore and present data while tools that support capturing and sharing gained insight have not received the same attention. Therefore, this is one of the focuses of the research presented in this thesis. A snapshot technique is introduced, which supports capturing discoveries made during the exploratory data analysis process and can be used for sharing gained knowledge. The thesis also presents a number of applications developed to verify the usability and the overall performance of the framework for the visualization, exploration and analysis of data in different domains. Four application scenarios are presented introducing (1) the synergies among information visualization methods, geovisualization methods and volume data visualization methods for the exploration and correlation of spatio-temporal ocean data, (2) effective techniques for the visualization, exploration and analysis of self-organizing network data, (3) effective flow visualization techniques applied to the analysis of time-varying spatial interaction data such as migration data, commuting data and trade flow data, and (4) effective techniques for the visualization, exploration and analysis of flood data.
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Reuterswärd, John. "Implementation & architecture of a cloud-based data analytics pipeline." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-301335.

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Organizations can gain deeper insight into the usage of their products and services by analyzing data from events as they happen in real-time. Examples of such data include user login attempts, feature usage, sign-ups, client logs as well as other domain and application specific events. The high frequency at which these types of events are generated combined with the large volume of data they will occupy once stored provides many interesting challenges. Most importantly, it is difficult to predict what type of analytics data will provide useful insights in the future. This master thesis documents the architecture of a data analytics pipeline based on the principles of decoupled components to meet this goal. It is shown that by extending the concepts of the publish & subscribe pattern and by leveraging cloud-based services the development of such a system is achievable even for smaller organizations.
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Ajiboye, Soladoye Oyebowale. "Video big data : an agile architecture for systematic exploration and analytics." Thesis, University of Sussex, 2017. http://sro.sussex.ac.uk/id/eprint/71047/.

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Video is currently at the forefront of most business and natural environments. In surveillance, it is the most important technology as surveillance systems reveal information and patterns for solving many security problems including crime prevention. This research investigates technologies that currently drive video surveillance systems with a view to optimization and automated decision support. The investigation reveals some features and properties that can be optimised to improve performance and derive further benefits from surveillance systems. These aspects include system-wide architecture, meta-data generation, meta-data persistence, object identification, object tagging, object tracking, search and querying sub-systems. The current less-than-optimum performance is attributable to many factors, which include massive volume, variety, and velocity (the speed at which streaming video transmit to storage) of video data in surveillance systems. Research contributions are 2-fold. First, we propose a system-wide architecture for designing and implementing surveillance systems, based on the authors' system architecture for generating meta-data. Secondly, we design a simulation model of a multi-view surveillance system from which the researchers generate simulated video streams in large volumes. From each video sequence in the model, the authors extract meta-data and apply a novel algorithm for predicting the location of identifiable objects across a well-connected camera cluster. This research provide evidence that independent surveillance systems (for example, security cameras) can be unified across a geographical location such as a smart city, where each network is administratively owned and managed independently. Our investigation involved 2 experiments - first, the implementation of a web-based solution where we developed a directory service for managing, cataloguing, and persisting metadata generated by the surveillance networks. The second experiment focused on the set up, configuration and the architecture of the surveillance system. These experiments involved the investigation and demonstration of 3 loosely coupled service-oriented APIs – these services provided the capability to generate the query-able metadata. The results of our investigations provided answers to our research questions - the main question being “to what degree of accuracy can we predict the location of an object in a connected surveillance network”. Our experiment also provided evidence in support of our hypothesis – “it is feasible to ‘explore' unified surveillance data generated from independent surveillance networks”.
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Carle, William R. II. "Active Analytics: Adapting Web Pages Automatically Based on Analytics Data." UNF Digital Commons, 2016. http://digitalcommons.unf.edu/etd/629.

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Web designers are expected to perform the difficult task of adapting a site’s design to fit changing usage trends. Web analytics tools give designers a window into website usage patterns, but they must be analyzed and applied to a website's user interface design manually. A framework for marrying live analytics data with user interface design could allow for interfaces that adapt dynamically to usage patterns, with little or no action from the designers. The goal of this research is to create a framework that utilizes web analytics data to automatically update and enhance web user interfaces. In this research, we present a solution for extracting analytics data via web services from Google Analytics and transforming them into reporting data that will inform user interface improvements. Once data are extracted and summarized, we expose the summarized reports via our own web services in a form that can be used by our client side User Interface (UI) framework. This client side framework will dynamically update the content and navigation on the page to reflect the data mined from the web usage reports. The resulting system will react to changing usage patterns of a website and update the user interface accordingly. We evaluated our framework by assigning navigation tasks to users on the UNF website and measuring the time it took them to complete those tasks, one group with our framework enabled, and one group using the original website. We found that the group that used the modified version of the site with our framework enabled was able to navigate the site more quickly and effectively.
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Villalon, Rachelle B. (Rachelle Bentajado). "Data mining, inference, and predictive analytics for the built environment with images, text, and WiFi data." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/115448.

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Thesis: Ph. D. in Architecture Design and Computation, Massachusetts Institute of Technology, Department of Architecture, June 2017.
Cataloged from PDF version of thesis. "February 2017."
Includes bibliographical references (pages 190-194).
What can campus WiFi data tell us about life at MIT? What can thousands of images tell us about the way people see and occupy buildings in real-time? What can we learn about the buildings that millions of people snap pictures of and text about over time? Crowdsourcing has triggered a dramatic shift in the traditional forms of producing content. The increasing number of people contributing to the Internet has created big data that has the potential to 1) enhance the traditional forms of spatial information that the design and engineering fields are typically accustomed to; 2) yield further insights about a place or building from discovering relationships between the datasets. In this research, I explore how the Architecture, Engineering, and Construction (AEC) industry can exploit crowdsourced and non-traditional datasets. I describe its possible roles for the following constituents: historian, designer/city administrator, and facilities manager - roles that engage with a building's information in the past, present, and future with different goals. As part of this research, I have developed a complete software pipeline for data mining, analyzing, and visualizing large volumes of crowdsourced unstructured content about MIT and other locations from images, campus WiFi access points, and text in batch/real-time using computer vision, machine learning, and statistical modeling techniques. The software pipeline is used for exploring meaningful statistical patterns from the processed data.
by Rachelle B. Villalon.
Ph. D. in Architecture Design and Computation
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O'Toole, Ryan (Ryan Michael). "Red Ink : open source financial analytics for people & communities." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62079.

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Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2010.
Cataloged from PDF version of thesis.
Includes bibliographical references.
Red Ink is an open source social-financial web-service that enables people to share, aggregate, analyze, visualize and publish their financial transactions as individuals and ad-hoc groups, through data sharing campaigns. Virtual and geographic communities of financial data sharers can form on Red Ink to create new sources of information for self-knowledge and understanding of complex personal, community, economic, environmental, and civic concerns and how to better coordinate their solutions. Red Ink posits that just like volunteering time or donating money, personal financial data is itself an asset that people can share to gain group leverage. Further, in the hands of everyday people, the data and tools of corporate scale consumer analysis will be reborn to serve larger and more personally meaningful goals.
by Ryan O'Toole.
S.M.
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Anne, Chaitanya. "Advanced Text Analytics and Machine Learning Approach for Document Classification." ScholarWorks@UNO, 2017. http://scholarworks.uno.edu/td/2292.

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Text classification is used in information extraction and retrieval from a given text, and text classification has been considered as an important step to manage a vast number of records given in digital form that is far-reaching and expanding. This thesis addresses patent document classification problem into fifteen different categories or classes, where some classes overlap with other classes for practical reasons. For the development of the classification model using machine learning techniques, useful features have been extracted from the given documents. The features are used to classify patent document as well as to generate useful tag-words. The overall objective of this work is to systematize NASA’s patent management, by developing a set of automated tools that can assist NASA to manage and market its portfolio of intellectual properties (IP), and to enable easier discovery of relevant IP by users. We have identified an array of methods that can be applied such as k-Nearest Neighbors (kNN), two variations of the Support Vector Machine (SVM) algorithms, and two tree based classification algorithms: Random Forest and J48. The major research steps in this work consist of filtering techniques for variable selection, information gain and feature correlation analysis, and training and testing potential models using effective classifiers. Further, the obstacles associated with the imbalanced data were mitigated by adding synthetic data wherever appropriate, which resulted in a superior SVM classifier based model.
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Flöring, Stefan [Verfasser], Hans-Jürgen [Akademischer Betreuer] Appelrath, and Tobias [Akademischer Betreuer] Isenberg. "KnoVA: a Reference Architecture for Knowledge-based Visual Analytics / Stefan Flöring. Betreuer: Hans-Jürgen Appelrath ; Tobias Isenberg." Oldenburg : BIS der Universität Oldenburg, 2012. http://d-nb.info/1050299485/34.

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Flöring, Stefan Verfasser], Hans-Jürgen [Akademischer Betreuer] [Appelrath, and Tobias [Akademischer Betreuer] Isenberg. "KnoVA: a Reference Architecture for Knowledge-based Visual Analytics / Stefan Flöring. Betreuer: Hans-Jürgen Appelrath ; Tobias Isenberg." Oldenburg : BIS der Universität Oldenburg, 2012. http://nbn-resolving.de/urn:nbn:de:gbv:715-oops-15310.

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Moscoso-Zea, Oswaldo. "A Hybrid Infrastructure of Enterprise Architecture and Business Intelligence & Analytics to Empower Knowledge Management in Education." Doctoral thesis, Universidad de Alicante, 2019. http://hdl.handle.net/10045/97408.

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The large volumes of data (Big Data) that are generated on a global scale and within organizations along with the knowledge that resides in people and in business processes makes organizational knowledge management (KM) very complex. A right KM can be a source of opportunities and competitive advantage for organizations that use their data intelligently and subsequently generate knowledge with them. Two of the fields that support KM and that have had accelerated growth in recent years are business intelligence (BI) and enterprise architecture (EA). On the one hand, BI allows taking advantage of the information stored in data warehouses using different operations such as slice, dice, roll-up, and drill-down. This information is obtained from the operational databases through an extraction, transformation, and loading (ETL) process. On the other hand, EA allows institutions to establish methods that support the creation, sharing and transfer of knowledge that resides in people and processes through the use of blueprints and models. One of the objectives of KM is to create a culture where tacit knowledge (knowledge that resides in a person) stays in an organization when qualified and expert personnel leave the institution or when changes are required in the organizational structure, in computer applications or in the technological infrastructure. In higher education institutions (HEIs) not having an adequate KM approach to handle data is even a greater problem due to the nature of this industry. Generally, HEIs have very little interdependence between departments and faculties. In other words, there is low standardization, redundancy of information, and constant duplicity of applications and functionalities in the different departments which causes inefficient organizations. That is why the research performed within this dissertation has focused on finding an adequate KM method and researching on the right technological infrastructure that supports the management of information of all the knowledge dimensions such as people, processes and technology. All of this with the objective to discover innovative mechanisms to improve education and the service that HEIs offer to their students and teachers by improving their processes. Despite the existence of some initiatives, and papers on KM frameworks, we were not able to find a standard framework that supports or guides KM initiatives. In addition, KM frameworks found in the literature do not present practical mechanisms to gather and analyze all the knowledge dimensions to facilitate the implementation of KM projects. The core contribution of this thesis is a hybrid infrastructure of KM based on EA and BI that was developed from research using an empirical approach and taking as reference the framework developed for KM. The proposed infrastructure will help HEIs to improve education in a general way by analyzing reliable and cleaned data and integrating analytics from the perspective of EA. EA analytics takes into account the interdependence between the objects that make up the organization: people, processes, applications, and technology. Through the presented infrastructure, the doors are opened for the realization of different research projects that increment the type of knowledge that is generated by integrating the information of the applications found in the data warehouses together with the information of the people and the organizational processes that are found in the EA repositories. In order to validate the proposal, a case study was carried out within a university with promising initial results. As future works, it is planned that different HEIs' activities can be automated through a software development methodology based on EA models. In addition, it is desired to develop a KM system that allows the generation of different and new types of analytics, which would be impossible to obtain with only transactional or multidimensional databases.
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Books on the topic "Architecture analytics"

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Chattopadhyay, Anupam, Chip Hong Chang, and Hao Yu, eds. Emerging Technology and Architecture for Big-data Analytics. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54840-1.

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Tache, Nicole, ed. Getting Started with Kudu: Perform Fast Analytics on Fast Data. Beijing: O’Reilly Media, 2018.

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Khan, Ahmad Nabi. Islamic architecture of Pakistan: An analytical exposition. Islamabad: National Hijra Council, 1990.

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Michael, Pause, ed. Precedents in architecture: Analytic diagrams, formative ideas, and partis. 4th ed. Hoboken, N.J: John Wiley & Sons, 2012.

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Leslie, Lewis, Schrader John Y, United States. Joint Chiefs of Staff. Joint Staff., National Defense Research Institute (U.S.), and Rand Corporation, eds. Analytic architecture for Joint Staff decision support activities: Final report. Santa Monica, CA: RAND, 1996.

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service), SpringerLink (Online, ed. Social Network Data Analytics. Boston, MA: Springer Science+Business Media, LLC, 2011.

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Biophilic and bioclimatic architecture: Analytical therapy for the next generation of passive sustainable architecture. London: Springer, 2011.

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CAD principles for architectural design: Analytical approaches to computational representation of architectural form. Oxford: Architectural Press, 2001.

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Architectural interior systems workbook: Analytical procedures and examples. New York: Van Nostrand Reinhold, 1992.

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Cieciek, Greg. LARE review.: Analytical aspects of practice. Belmont, CA: Professional Publications, 2005.

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Book chapters on the topic "Architecture analytics"

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Huang, Shuai, and Houtao Deng. "Synthesis Architecture & Pipeline." In Data Analytics, 219–46. First edition. | Boca Raton : CRC Press, 2021.: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003102656-ch10.

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Martella, Claudio, Roman Shaposhnik, and Dionysios Logothetis. "Giraph Architecture." In Practical Graph Analytics with Apache Giraph, 137–62. Berkeley, CA: Apress, 2015. http://dx.doi.org/10.1007/978-1-4842-1251-6_6.

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Cheramangalath, Unnikrishnan, Rupesh Nasre, and Y. N. Srikant. "GPU Architecture and Programming Challenges." In Distributed Graph Analytics, 123–36. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-41886-1_5.

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Platenius-Mohr, Marie, Hadil Abukwaik, Jan Schlake, and Michael Vach. "Software Architectures for Edge Analytics: A Survey." In Software Architecture, 295–311. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86044-8_20.

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Castellanos, Camilo, Dario Correal, and Juliana-Davila Rodriguez. "Executing Architectural Models for Big Data Analytics." In Software Architecture, 364–71. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00761-4_24.

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Nath, Shyam Varan. "IoT ARCHITECTURE." In Internet of Things and Data Analytics Handbook, 239–49. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119173601.ch14.

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Castellanos, Camilo, Boris Pérez, Carlos A. Varela, María del Pilar Villamil, and Dario Correal. "A Survey on Big Data Analytics Solutions Deployment." In Software Architecture, 195–210. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29983-5_13.

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Garcia-Bedoya, Olmer, Janito Vaqueiro Ferreira, Nicolas Clavijo-Buritica, Edgar Gutierrez-Franco, and Larry Lowe. "Deep Neural Networks Applied in Autonomous Vehicle Software Architecture." In Engineering Analytics, 197–215. New York: CRC Press, 2021. http://dx.doi.org/10.1201/9781003137993-13.

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Shiyal, Bhadresh. "Architecture and Its Main Components." In Beginning Azure Synapse Analytics, 69–86. Berkeley, CA: Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7061-5_4.

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Kumar, Aditya, and Satish Narayana Srirama. "Fog Enabled Distributed Training Architecture for Federated Learning." In Big Data Analytics, 78–92. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93620-4_7.

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Conference papers on the topic "Architecture analytics"

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Kandogan, Eser, Mary Roth, Ignacio Terrizzano, Ed Seabolt, Peter Schwarz, Harsha Krishnareddy, and Akshay Agarwal. "Context Analytics: Vision, Architecture, Opportunity." In 2018 IEEE 34th International Conference on Data Engineering Workshops (ICDEW). IEEE, 2018. http://dx.doi.org/10.1109/icdew.2018.00007.

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Chen, Shih-Yu, Gwo Giun Chris Lee, Tai-Ping Wang, Chin-Wei Huang, Jia-Hong Chen, and Chang-Ling Tsai. "Reconfigurable Edge via Analytics Architecture." In 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS). IEEE, 2019. http://dx.doi.org/10.1109/aicas.2019.8771528.

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Perera, Rohan D. W., S. Anand, K. P. Subbalakshmi, and R. Chandramouli. "Twitter analytics: Architecture, tools and analysis." In MILCOM 2010 - 2010 IEEE Military Communications Conference. IEEE, 2010. http://dx.doi.org/10.1109/milcom.2010.5680493.

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Zaghloul, Mohamed M., Amr Ali-Eldin, and Mofreh Salem. "A process-centric data analytics architecture." In 2014 9th International Conference on Informatics and Systems (INFOS). IEEE, 2014. http://dx.doi.org/10.1109/infos.2014.7036705.

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Cabezas, Ivan, and Julian Palacios. "A Software Architecture for Video Analytics." In 2020 10th International Conference on Advanced Computer Information Technologies (ACIT). IEEE, 2020. http://dx.doi.org/10.1109/acit49673.2020.9208938.

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Rahman, Md Mijanur, and Md Hasibul Hasan. "Serverless Architecture for Big Data Analytics." In 2019 Global Conference for Advancement in Technology (GCAT). IEEE, 2019. http://dx.doi.org/10.1109/gcat47503.2019.8978443.

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Fuentalba, Diego, Claudia Duran, Charles Guillaume, Raul Carrasco, Sebastian Gutierrez, and Oscar Pinto. "Text Analytics Architecture in IoT Systems." In 2021 Third South American Colloquium on Visible Light Communications (SACVLC). IEEE, 2021. http://dx.doi.org/10.1109/sacvlc53127.2021.9652319.

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Salapura, Valentina, Tejas Karkhanis, Priya Nagpurkar, and Jose Moreira. "Accelerating business analytics applications." In 2012 IEEE 18th International Symposium on High Performance Computer Architecture (HPCA). IEEE, 2012. http://dx.doi.org/10.1109/hpca.2012.6169044.

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Harper, K. Eric, and Aldo Dagnino. "Agile Software Architecture in Advanced Data Analytics." In 2014 IEEE/IFIP Conference on Software Architecture (WICSA). IEEE, 2014. http://dx.doi.org/10.1109/wicsa.2014.16.

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Gomez-Zamora, Paula, Sonit Bafna, Craig Zimring, Ellen Yi-Luen Do, and Mario Romero Vega. "Spatiotemporal Occupancy for Building Analytics." In eCAADe 2019: Architecture in the Age of the 4th Industrial Revolution. eCAADe, 2019. http://dx.doi.org/10.52842/conf.ecaade.2019.2.111.

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Reports on the topic "Architecture analytics"

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Mazorchuk, Mariia S., Tetyana S. Vakulenko, Anna O. Bychko, Olena H. Kuzminska, and Oleksandr V. Prokhorov. Cloud technologies and learning analytics: web application for PISA results analysis and visualization. [б. в.], June 2021. http://dx.doi.org/10.31812/123456789/4451.

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This article analyzes the ways to apply Learning Analytics, Cloud Technologies, and Big Data in the field of education on the international level. This paper provides examples of international analytical researches and cloud technologies used to process the results of those researches. It considers the PISA research methodology and related tools, including the IDB Analyzer application, free R intsvy environment for processing statistical data, and cloud-based web application PISA Data Explorer. The paper justifies the necessity of creating a stand-alone web application that supports Ukrainian localization and provides Ukrainian researchers with rapid access to well-structured PISA data. In particular, such an application should provide for data across the factorial features and indicators applied at the country level and demonstrate the Ukrainian indicators compared to the other countries’ results. This paper includes a description of the application core functionalities, architecture, and technologies used for development. The proposed solution leverages the shiny package available with R environment that allows implementing both the UI and server sides of the application. The technical implementation is a proven solution that allows for simplifying the access to PISA data for Ukrainian researchers and helping them utilize the calculation results on the key features without having to apply tools for processing statistical data.
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Winder, Ransom, Nathan Giles, and Joseph Jubinski. Implementation Recommendations for MOSAIC: A Workflow Architecture for Analytic Enrichment. Analysis and Recommendations for the Implementation of a Cohesive Method for Orchestrating Analytics in a Distributed Model. Fort Belvoir, VA: Defense Technical Information Center, February 2011. http://dx.doi.org/10.21236/ada586570.

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Chiang, Mung. Towards an Analytic Foundation for Network Architecture. Fort Belvoir, VA: Defense Technical Information Center, December 2010. http://dx.doi.org/10.21236/ada535200.

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GALAMBOS, PAUL C., ARTHUR E. PONTAU, MURAT OKANDAN, CONRAD D. JAMES, BRIAN J. KIRBY, TIMOTHY J. SHEPODD, and KENNETH R. POHL. New Architectures for Micro-Total-Analytical Systems. Office of Scientific and Technical Information (OSTI), December 2002. http://dx.doi.org/10.2172/805882.

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Cardwell, Suma, Mark Plagge, Clayton Hughes, Fredrick Rothganger, Sapan Agarwal, Benjamin Feinberg, Amro Awad, John McFarland, and Luke Parker. ATHENA: Analytical Tool for Heterogeneous Neuromorphic Architectures. Office of Scientific and Technical Information (OSTI), September 2022. http://dx.doi.org/10.2172/1890038.

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Laird, John E. Research in Architectural Approaches to the Integration of Empirical, Analytic and Episodic Learning within SOAR. Fort Belvoir, VA: Defense Technical Information Center, May 2005. http://dx.doi.org/10.21236/ada434996.

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Pris, Andrew David. Miniaturized Analytical Platforms From Nanoparticle Components: Studies in the Construction, Characterization, and High-Throughput Usage of These Novel Architectures. Office of Scientific and Technical Information (OSTI), January 2003. http://dx.doi.org/10.2172/816448.

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Lewis, Dustin, Radhika Kapoor, and Naz Modirzadeh. Advancing Humanitarian Commitments in Connection with Countering Terrorism: Exploring a Foundational Reframing concerning the Security Council. Harvard Law School Program on International Law and Armed Conflict, December 2021. http://dx.doi.org/10.54813/uzav2714.

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The imperative to provide humanitarian and medical services on an urgent basis in armed conflicts is anchored in moral tenets, shared values, and international rules. States spend tens of billions of dollars each year to help implement humanitarian programs in conflicts across the world. Yet, in practice, counterterrorism objectives increasingly prevail over humanitarian concerns, often resulting in devastating effects for civilian populations in need of aid and protection in war. Not least, confusion and misapprehensions about the power and authority of States relative to the United Nations Security Council to set policy preferences and configure legal obligations contribute significantly to this trajectory. In this guide for States, we present a framework to reconfigure relations between these core commitments by assessing the counterterrorism architecture through the lens of impartial humanitarianism. We aim in particular to provide an evidence base and analytical frame for States to better grasp key legal and policy issues related to upholding respect for principled humanitarian action in connection with carrying out the Security Council’s counterterrorism decisions. We do so because the lack of knowledge regarding interpretation and implementation of counterterrorism resolutions matters for the coherence, integrity, and comprehensiveness of humanitarian policymaking and protection of the humanitarian imperative. In addition to analyzing foundational concerns and evaluating discernible behaviors and attitudes, we identify avenues that States may take to help achieve pro-humanitarian objectives. We also endeavor to help disseminate indications of, and catalyze, States’ legally relevant positions and practices on these issues. In section 1, we introduce the guide’s impetus, objectives, target audience, and structure. We also describe the methods that we relied on and articulate definitions for key terms. In section 2, we introduce key legal actors, sources of law, and the notion of international legal responsibility, as well as the relations between international and national law. Notably, Security Council resolutions require incorporation into national law in order to become effective and enforceable by internal administrative and judicial authorities. In section 3, we explain international legal rules relevant to advancing the humanitarian imperative and upholding respect for principled humanitarian action, and we sketch the corresponding roles of humanitarian policies, programs, and donor practices. International humanitarian law (IHL) seeks to ensure — for people who are not, or are no longer, actively participating in hostilities and whose needs are unmet — certain essential supplies, as well as medical care and attention for the wounded and sick. States have also developed and implemented a range of humanitarian policy frameworks to administer principled humanitarian action effectively. Further, States may rely on a number of channels to hold other international actors to account for safeguarding the humanitarian imperative. In section 4, we set out key theoretical and doctrinal elements related to accepting and carrying out the Security Council’s decisions. Decisions of the Security Council may contain (binding) obligations, (non-binding) recommendations, or a combination of the two. UN members are obliged to carry out the Council’s decisions. Member States retain considerable interpretive latitude to implement counterterrorism resolutions. With respect to advancing the humanitarian imperative, we argue that IHL should represent a legal floor for interpreting the Security Council’s decisions and recommendations. In section 5, we describe relevant conduct of the Security Council and States. Under the Resolution 1267 (1999), Resolution 1989 (2011), and Resolution 2253 (2015) line of resolutions, the Security Council has established targeted sanctions as counterterrorism measures. Under the Resolution 1373 (2001) line of resolutions, the Security Council has adopted quasi-“legislative” requirements for how States must counter terrorism in their national systems. Implementation of these sets of resolutions may adversely affect principled humanitarian action in several ways. Meanwhile, for its part, the Security Council has sought to restrict the margin of appreciation of States to determine how to implement these decisions. Yet international law does not demand that these resolutions be interpreted and implemented at the national level by elevating security rationales over policy preferences for principled humanitarian action. Indeed, not least where other fields of international law, such as IHL, may be implicated, States retain significant discretion to interpret and implement these counterterrorism decisions in a manner that advances the humanitarian imperative. States have espoused a range of views on the intersections between safeguarding principled humanitarian action and countering terrorism. Some voice robust support for such action in relation to counterterrorism contexts. A handful call for a “balancing” of the concerns. And some frame respect for the humanitarian imperative in terms of not contradicting counterterrorism objectives. In terms of measures, we identify five categories of potentially relevant national counterterrorism approaches: measures to prevent and suppress support to the people and entities involved in terrorist acts; actions to implement targeted sanctions; measures to prevent and suppress the financing of terrorism; measures to prohibit or restrict terrorism-related travel; and measures that criminalize or impede medical care. Further, through a number of “control dials” that we detect, States calibrate the functional relations between respect for principled humanitarian action and countering terrorism. The bulk of the identified counterterrorism measures and related “control dials” suggests that, to date, States have by and large not prioritized advancing respect for the humanitarian imperative at the national level. Finally, in section 6, we conclude by enumerating core questions that a State may answer to help formulate and instantiate its values, policy commitments, and legal positions to secure respect for principled humanitarian action in relation to counterterrorism contexts.
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