Bibliografias temáticas / Data models, storage and indexing

Literatura científica selecionada sobre o tema "Data models, storage and indexing"

Autor: Grafiati
Publicado: 17 de julho de 2024

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Artigos de revistas sobre o assunto "Data models, storage and indexing"

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Wang, Zhaoguo, Haibo Chen, Youyun Wang, Chuzhe Tang e Huan Wang. "The Concurrent Learned Indexes for Multicore Data Storage". ACM Transactions on Storage 18, n.º 1 (28 de fevereiro de 2022): 1–35. http://dx.doi.org/10.1145/3478289.

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We present XIndex, which is a concurrent index library and designed for fast queries. It includes a concurrent ordered index (XIndex-R) and a concurrent hash index (XIndex-H). Similar to a recent proposal of the learned index, the indexes in XIndex use learned models to optimize index efficiency. Compared with the learned index, for the ordered index, XIndex-R is able to handle concurrent writes effectively and adapts its structure according to runtime workload characteristics. For the hash index, XIndex-H is able to avoid the resize operation blocking concurrent writes. Furthermore, the indexes in XIndex can index string keys much more efficiently than the learned index. We demonstrate the advantages of XIndex with YCSB, TPC-C (KV), which is a TPC-C-inspired benchmark for key-value stores, and micro-benchmarks. Compared with ordered indexes of Masstree and Wormhole, XIndex-R achieves up to 3.2× and 4.4× performance improvement on a 24-core machine. Compared with hash indexes of Intel TBB HashMap, XIndex-H achieves up to 3.1× speedup. The performance further improves by 91% after adding the optimizations on indexing string keys. The library is open-sourced. 1
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Selihat, Maali, Belal Abuhaja e Khalid Alkaabneh. "Secure audio file indexing based on hidden markov model (HMM) on the cloud storage". International Journal Artificial Intelligent and Informatics 2, n.º 1 (16 de julho de 2021): 1–8. http://dx.doi.org/10.33292/ijarlit.v2i1.30.

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With the introduction of many social media applications and the exponential growth of the number of people using such applications to exchange Audio files as a main way of conveying confidential messages relaying on public telecommunications and networks. The need arise to secure audio data files and preserve the integrity of the message while traversing public networks and when the data is at rest in the cloud. Therefore, in this research, to ensure confidentiality and integrity of the audio files while reducing storage space several algorithms have been devised. To achieve this, we utilized Public key infrastructure and Hash functions along with Hidden Markov Models (HMM). The results show a significant drop in the storage space needed while remarkable reduction in network transmission time. When comparing the original audio file size with the converted file size after applying HMM, the results show a variation between 0% and 10% only, with over 50% reduction in the storage space in some cases.
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Frady, E. Paxon, Denis Kleyko e Friedrich T. Sommer. "A Theory of Sequence Indexing and Working Memory in Recurrent Neural Networks". Neural Computation 30, n.º 6 (junho de 2018): 1449–513. http://dx.doi.org/10.1162/neco_a_01084.

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To accommodate structured approaches of neural computation, we propose a class of recurrent neural networks for indexing and storing sequences of symbols or analog data vectors. These networks with randomized input weights and orthogonal recurrent weights implement coding principles previously described in vector symbolic architectures (VSA) and leverage properties of reservoir computing. In general, the storage in reservoir computing is lossy, and crosstalk noise limits the retrieval accuracy and information capacity. A novel theory to optimize memory performance in such networks is presented and compared with simulation experiments. The theory describes linear readout of analog data and readout with winner-take-all error correction of symbolic data as proposed in VSA models. We find that diverse VSA models from the literature have universal performance properties, which are superior to what previous analyses predicted. Further, we propose novel VSA models with the statistically optimal Wiener filter in the readout that exhibit much higher information capacity, in particular for storing analog data. The theory we present also applies to memory buffers, networks with gradual forgetting, which can operate on infinite data streams without memory overflow. Interestingly, we find that different forgetting mechanisms, such as attenuating recurrent weights or neural nonlinearities, produce very similar behavior if the forgetting time constants are matched. Such models exhibit extensive capacity when their forgetting time constant is optimized for given noise conditions and network size. These results enable the design of new types of VSA models for the online processing of data streams.
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Ibtisam, Ferrahi Ibtisam, Sandro Bimonte e Kamel Boukhalfa. "Logical and Physical Design of Spatial Non-Strict Hierarchies in Relational Spatial Data Warehouse". International Journal of Data Warehousing and Mining 15, n.º 1 (janeiro de 2019): 1–18. http://dx.doi.org/10.4018/ijdwm.2019010101.

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The emergence of spatial or geographic data in DW Systems defines new models that support the storage and manipulation of the data. The need to build an SDW and to optimize SOLAP queries continues to attract the interest of researchers in recent years. Several spatial data models have been investigated to extend classical multidimensional data models with spatial concepts. However, most of existing models do not handle a non-strict spatial hierarchy. Moreover, the complexity of the spatial data makes the execution time of spatial queries very considerable. Often, spatial indexation methods are applied to optimizing access to large volumes of data and helps reduce the cost of spatial OLAP queries. Most of existing indexes support predefined spatial hierarchies. The authors show, in this article, that the logical models proposed in the literature and indexing techniques are not suitable to non-strict hierarchies. The authors propose a new logical schema supporting the non-strict hierarchies and a bitmap index to optimize queries defined by spatial dimensions with several non-strict hierarchies.
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Abebe, Michael, Horatiu Lazu e Khuzaima Daudjee. "Tiresias". Proceedings of the VLDB Endowment 15, n.º 11 (julho de 2022): 3126–36. http://dx.doi.org/10.14778/3551793.3551857.

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To efficiently store and query a DBMS, administrators must select storage and indexing configurations. For example, one must decide whether data should be stored in rows or columns, in-memory or on disk, and which columns to index. These choices can be challenging to make for workloads that are mixed requiring hybrid transactional and analytical processing (HTAP) support. There is growing interest in system designs that can adapt how data is stored and indexed to execute these workloads efficiently. We present Tiresias , a predictor that learns the cost of data accesses and predicts their latency and likelihood under different storage scenarios. Tiresias makes these predictions by collecting observed latencies and access histories to build predictive models in an online manner, enabling autonomous storage and index adaptation. Experimental evaluation shows the benefits of predictive adaptation and the trade-offs for different predictive techniques.
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Rao, Sirisala Nageswara. "Optimized Cost Model for k-NN Queries in R*-Trees over Random Distribution". Advanced Materials Research 403-408 (novembro de 2011): 3315–21. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3315.

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Efficient storage and retrieval of multidimensional data in large volumes has become one of the key issues in the design and implementation of commercial and application software. The kind of queries posted on such data is also multifarious. Nearest neighbor queries are one such category and have more significance in GIS type of application. R-tree and its sequel are data partitioned hierarchical multidimensional indexing structures that help in this purpose. Today’s research has turned towards the development of powerful analytical method to predict the performance of such indexing structures such as for varies categories of queries such as range, nearest neighbor, join, etc .This paper focuses on performance of R*-tree for k nearest neighbor (kNN) queries. While general approaches are available in literature that works better for larger k over uniform data, few have explored the impact of small values of k. This paper proposes improved performance analysis model for kNN query for small k over random data. The results are tabulated and compared with existing models, the proposed model out performs the existing models in a significant way for small k
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Ma, Chaohong, Xiaohui Yu, Yifan Li, Xiaofeng Meng e Aishan Maoliniyazi. "FILM". Proceedings of the VLDB Endowment 16, n.º 3 (novembro de 2022): 561–73. http://dx.doi.org/10.14778/3570690.3570704.

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As modern applications generate data at an unprecedented speed and often require the querying/analysis of data spanning a large duration, it is crucial to develop indexing techniques that cater to larger-than-memory databases, where data reside on heterogeneous storage devices (such as memory and disk), and support fast data insertion and query processing. In this paper, we propose FILM, a F ully learned I ndex for L arger-than- M emory databases. FILM is a learned tree structure that uses simple approximation models to index data spanning different storage devices. Compared with existing techniques for larger-than-memory databases, such as anti-caching, FILM allows for more efficient query processing at significantly lower main-memory overhead. FILM is also designed to effectively address one of the bottlenecks in existing methods for indexing larger-than-memory databases that is caused by data swapping between memory and disk. More specifically, updating the LRU (for Least Recently Used) structure employed by existing methods for cold data identification (determining the data to be evicted to disk when the available memory runs out) often incurs significant delay to query processing. FILM takes a drastically different approach by proposing an adaptive LRU structure and piggybacking its update onto query processing with minimal overhead. We thoroughly study the performance of FILM and its components on a variety of datasets and workloads, and the experimental results demonstrate its superiority in improving query processing performance and reducing index storage overhead (by orders of magnitudes) compared with applicable baselines.
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Ge, Jiake, Huanchen Zhang, Boyu Shi, Yuanhui Luo, Yunda Guo, Yunpeng Chai, Yuxing Chen e Anqun Pan. "SALI: A Scalable Adaptive Learned Index Framework based on Probability Models". Proceedings of the ACM on Management of Data 1, n.º 4 (8 de dezembro de 2023): 1–25. http://dx.doi.org/10.1145/3626752.

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The growth in data storage capacity and the increasing demands for high performance have created several challenges for concurrent indexing structures. One promising solution is the learned index, which uses a learning-based approach to fit the distribution of stored data and predictively locate target keys, significantly improving lookup performance. Despite their advantages, prevailing learned indexes exhibit constraints and encounter issues of scalability on multi-core data storage. This paper introduces SALI, the Scalable Adaptive Learned Index framework, which incorporates two strategies aimed at achieving high scalability, improving efficiency, and enhancing the robustness of the learned index. Firstly, a set of node-evolving strategies is defined to enable the learned index to adapt to various workload skews and enhance its concurrency performance in such scenarios. Secondly, a lightweight strategy is proposed to maintain statistical information within the learned index, with the goal of further improving the scalability of the index. Furthermore, to validate their effectiveness, SALI applied the two strategies mentioned above to the learned index structure that utilizes fine-grained write locks, known as LIPP. The experimental results have demonstrated that SALI significantly enhances the insertion throughput with 64 threads by an average of 2.04x compared to the second-best learned index. Furthermore, SALI accomplishes a lookup throughput similar to that of LIPP+.
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Boguslawski, P., P. Balak e C. Gold. "DATABASE STORAGE AND TRANSPARENT MEMORY LOADING OF BIG SPATIAL DATASETS IMPLEMENTED WITH THE DUAL HALF-EDGE DATA STRUCTURE". International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022 (1 de junho de 2022): 9–15. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2022-9-2022.

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Abstract. 3D spatial models covering big areas, such as cities, are widely developed in recent years. Loading of a whole model from a hard drive into a computer memory is often not possible due to big amount of data and memory size limitations. Optimisation techniques based on spatial indexing, such as tiling, are applied in order to load at least a part of a model as soon as possible, while the remaining parts are collected in the background. It is especially useful in visualisation of cities. A similar idea is proposed for a transparent loading of a model implemented with the dual half-edge (DHE) data structure and stored in a database. The existing DHE-based solutions require the whole model to be present in the memory, which is a considerable limitation in case of models covering big areas and including detailed representations of city objects, such as buildings and their interiors. The prototype mechanism developed in this work includes loading and unloading of model parts at a level of single edges as well as model tiling. This allows for spatial analysis without complete loading a big amount of data into memory.
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Ramzan, Bajwa, Kazmi e Amna. "Challenges in NoSQL-Based Distributed Data Storage: A Systematic Literature Review". Electronics 8, n.º 5 (30 de abril de 2019): 488. http://dx.doi.org/10.3390/electronics8050488.

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Key-Value stores (KVSs) are the most flexible and simplest model of NoSQL databases, which have become highly popular over the last few years due to their salient features such as availability, portability, reliability, and low operational cost. From the perspective of software engineering, the chief obstacle for KVSs is to achieve software quality attributes (consistency, throughput, latency, security, performance, load balancing, and query processing) to ensure quality. The presented research is a Systematic Literature Review (SLR) to find the state-of-the-art research in the KVS domain, and through doing so determine the major challenges and solutions. This work reviews the 45 papers between 2010–2018 that were found to be closely relevant to our study area. The results show that performance is addressed in 31% of the studies, consistency is addressed in 20% of the studies, latency and throughput are addressed in 16% of the studies, query processing is addressed in 13% of studies, security is addressed in 11% of the studies, and load balancing is addressed in 9% of the studies. Different models are used for execution. The indexing technique was used in 20% of the studies, the hashing technique was used in 13% of the studies, the caching and security techniques were used together in 9% of the studies, the batching technique was used in 5% of the studies, the encoding techniques and Paxos technique were used together in 4% of the studies, and 36% of the studies used other techniques. This systematic review will enable researchers to design key-value stores as efficient storage. Regarding future collaborations, trust and privacy are the quality attributes that can be addressed; KVS is an emerging facet due to its widespread popularity, opening the way to deploy it with proper protection.
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Teses / dissertações sobre o assunto "Data models, storage and indexing"

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Munishwar, Vikram P. "Storage and indexing issues in sensor networks". Diss., Online access via UMI:, 2006.

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Ottoson, Patrik. "Geographic Indexing and Data Management for 3D-Visualisation". Doctoral thesis, Stockholm : Tekniska högsk, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3235.

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Vasaitis, Vasileios. "Novel storage architectures and pointer-free search trees for database systems". Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/6240.

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Database systems research is an old and well-established field in computer science. Many of the key concepts appeared as early as the 60s, while the core of relational databases, which have dominated the database world for a while now, was solidified during the 80s. However, the underlying hardware has not displayed such stability in the same period, which means that a lot of assumptions that were made about the hardware by early database systems are not necessarily true for modern computer architectures. In particular, over the last few decades there have been two notable consistent trends in the evolution of computer hardware. The first is that the memory hierarchy of mainstream computer systems has been getting deeper, with its different levels moving away from each other, and new levels being added in between as a result, in particular cache memories. The second is that, when it comes to data transfers between any two adjacent levels of the memory hierarchy, access latencies have not been keeping up with transfer rates. The challenge is therefore to adapt database index structures so that they become immune to these two trends. The latter is addressed by gradually increasing the size of the data transfer unit; the former, by organizing the data so that it exhibits good locality for memory transfers across multiple memory boundaries. We have developed novel structures that facilitate both of these strategies. We started our investigation with the venerable B+-tree, which is the cornerstone order-preserving index of any database system, and we have developed a novel pointer-free tree structure for its pages that optimizes its cache performance and makes it immune to the page size. We then adapted our approach to the R-tree and the GiST, making it applicable to multi-dimensional data indexes as well as generalized indexes for any abstract data type. Finally, we have investigated our structure in the context of main memory alone, and have demonstrated its superiority over the established approaches in that setting too. While our research has its roots in data structures and algorithms theory, we have conducted it with a strong experimental focus, as the complex interactions within the memory hierarchy of a modern computer system can be quite challenging to model and theorize about effectively. Our findings are therefore backed by solid experimental results that verify our hypotheses and prove the superiority of our structures over competing approaches.
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Jia, Yanan Jia. "Generalized Bilinear Mixed-Effects Models for Multi-Indexed Multivariate Data". The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1469180629.

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Habtu, Simon. "Indexing file metadata using a distributed search engine for searching files on a public cloud storage". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-232064.

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Visma Labs AB or Visma wanted to conduct experiments to see if file metadata could be indexed for searching files on a public cloud storage. Given that storing files in a public cloud storage is cheaper than the current storage solution, the implementation could save Visma money otherwise spent on expensive storage costs. The thesis is therefore to find and evaluate an approach chosen for indexing file metadata and searching files on a public cloud storage with the chosen distributed search engine Elasticsearch. The architecture of the proposed solution is similar to a file service and was implemented using several containerized services for it to function. The results show that the file service solution is indeed feasible but would need further tuning and more resources to function according to the demands of Visma.
Visma Labs AB eller Visma ville genomföra experiment för att se om filmetadata skulle kunna indexeras för att söka efter filer på ett publikt moln. Med tanke på att lagring av filer på ett publikt moln är billigare än den nuvarande lagringslösningen, kan implementeringen spara Visma pengar som spenderas på dyra lagringskostnader. Denna studie är därför till för att hitta och utvärdera ett tillvägagångssätt valt för att indexera filmetadata och söka filer på ett offentligt molnlagring med den utvalda distribuerade sökmotorn Elasticsearch. Arkitekturen för den föreslagna lösningen har likenelser av en filtjänst och implementerades med flera containeriserade tjänster för att den ska fungera. Resultaten visar att filservicelösningen verkligen är möjlig men skulle behöva ytterligare modifikationer och fler resurser att fungera enligt Vismas krav.
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Singh, Aameek. "Secure Management of Networked Storage Services: Models and Techniques". Diss., Available online, Georgia Institute of Technology, 2007, 2007. http://etd.gatech.edu/theses/available/etd-04092007-004039/.

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Thesis (Ph. D.)--Computing, Georgia Institute of Technology, 2008.
Liu, Ling, Committee Chair ; Aberer, Karl, Committee Member ; Ahamad, Mustaque, Committee Member ; Blough, Douglas, Committee Member ; Pu, Calton, Committee Member ; Voruganti, Kaladhar, Committee Member.
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Paul, Arnab Kumar. "An Application-Attuned Framework for Optimizing HPC Storage Systems". Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99793.

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High performance computing (HPC) is routinely employed in diverse domains such as life sciences, and Geology, to simulate and understand the behavior of complex phenomena. Big data driven scientific simulations are resource intensive and require both computing and I/O capabilities at scale. There is a crucial need for revisiting the HPC I/O subsystem to better optimize for and manage the increased pressure on the underlying storage systems from big data processing. Extant HPC storage systems are designed and tuned for a specific set of applications targeting a range of workload characteristics, but they lack the flexibility in adapting to the ever-changing application behaviors. The complex nature of modern HPC storage systems along with the ever-changing application behaviors present unique opportunities and engineering challenges. In this dissertation, we design and develop a framework for optimizing HPC storage systems by making them application-attuned. We select three different kinds of HPC storage systems - in-memory data analytics frameworks, parallel file systems and object storage. We first analyze the HPC application I/O behavior by studying real-world I/O traces. Next we optimize parallelism for applications running in-memory, then we design data management techniques for HPC storage systems, and finally focus on low-level I/O load balance for improving the efficiency of modern HPC storage systems.
Doctor of Philosophy
Clusters of multiple computers connected through internet are often deployed in industry and laboratories for large scale data processing or computation that cannot be handled by standalone computers. In such a cluster, resources such as CPU, memory, disks are integrated to work together. With the increase in popularity of applications that read and write a tremendous amount of data, we need a large number of disks that can interact effectively in such clusters. This forms the part of high performance computing (HPC) storage systems. Such HPC storage systems are used by a diverse set of applications coming from organizations from a vast range of domains from earth sciences, financial services, telecommunication to life sciences. Therefore, the HPC storage system should be efficient to perform well for the different read and write (I/O) requirements from all the different sets of applications. But current HPC storage systems do not cater to the varied I/O requirements. To this end, this dissertation designs and develops a framework for HPC storage systems that is application-attuned and thus provides much improved performance than other state-of-the-art HPC storage systems without such optimizations.
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Regin, Måns, e Gunnarsson Emil. "Refactoring Existing Database Layers for Improved Performance, Readability and Simplicity". Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-105277.

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Since the late 90s, support and services at SAAB have produced and maintained a product called ELDIS. ELDIS is an application used by Swedish armed forces at air bases in Sweden and flight technicians at air bases. It displays electrical information, wire diagrams, and detailed information for cables, electrical equipment, and other electrical devices. The main problem for ELDIS is that when drawing wire diagrams in the application, it takes too long of a time when the stored procedures are retrieving information from the database. There are two significant areas in this project, analyzing and optimizing stored procedures and implementing a client-side solution. This project aims to guide SAAB to choose the right approach for solving the performance issue of the application and display some of the problems that can exist with slow stored procedures for companies in general. This project has optimized the most used stored procedure at SAAB and compared it to a client-side solution and the original application. The result of this project is that both the optimized stored procedure implementation and the client-side implementation is a faster option than the original implementation. It also highlights that when trying to optimize the stored procedures, indexing on the database should be considered for increasing the performance of a stored procedure.
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Chan, Wing Sze. "Semantic search of multimedia data objects through collaborative intelligence". HKBU Institutional Repository, 2010. http://repository.hkbu.edu.hk/etd_ra/1171.

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Tandon, Ashish. "Analysis and optimization of data storage using enhanced object models in the .NET framework". Thesis, Edinburgh Napier University, 2007. http://researchrepository.napier.ac.uk/Output/4047.

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The purpose of thesis is to benchmark the database to examine and analyze the performance using the Microsoft COM+ the most commonly used component framework heavily used for developing component based applications. The prototype application based on Microsoft Visual C#.NET language used to benchmark the database performance on Microsoft .NET Framework environment 2.0 and 3.0 using the different sizes of data range from low (100 Rows) to high volume (10000 Rows) of data with five or ten number of users connections. There are different type of application used like COM+, Non-COM+ and .NET based application to show their performance on the different volume of data with specified numbers of user on the .NET Framework 2.0 and 3.0. The result has been analyzed and collected using the performance counter variables of an operating system and used Microsoft .NET class libraries which help in collecting system's level performance information as well. This can be beneficial to developers, stakeholders and management to decide the right technology to be used in conjunction with a database. The results and experiments conducted in this project results in the substantial gain in the performance, scalability and availability of component based application using the Microsoft COM+ features like object pooling, application pooling, role- based, transactions isolation and constructor enabled. The outcome of this project is that Microsoft COM+ component based application provides optimized database performance results using the SQL Server. There is a performance gain of at least 10% in the COM+ based application as compared to the Non COM+ based application. COM+ services features come at the performance penalty. It has been noticed that there is a performance difference between the COM+ based application and the application based on role based security, constructor enable and transaction isolation of around 15%, 20% and 35% respectively. The COM+ based application provides performance gain of around 15% and 45% on the low and medium volume of data on a .NET Framework 2.0 in comparison to 3.0. There is a significant gain in the COM+ Server based application on .NET Framework 3.0 of around 10% using high volume of data. This depicts that high volume of data application works better with Framework 3.0 as compared to 2.0 on SQL Server. The application performance type results represents that COM+ component based application provides better performance results over Non-COM+ and .NET based application. The difference between the performance of COM+ application based on low and medium volume of data was around 20% and 30%. .NET based application performs better on the high volume of data results in performance gain of around 10%. Similarly more over the same results provided on the test conducted on the MS Access. Where COM+ based application running under .NET Framework 2.0 performs better result other than the Non-COM+ and .NET based application on a low and medium volume of data and .NET Framework 3.0 based COM+ application performs better results on high volume of data.
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Livros sobre o assunto "Data models, storage and indexing"

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Institute, Environmental Systems Research, ed. ARC/INFO data management: Concepts, data models, database design, and storage. Redlands, Calif: ESRI, 1994.

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Manolopoulos, Yannis. Advanced database indexing. Boston: Kluwer Academic, 2000.

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Cisco, Susan Lynn. Indexing business records: The value proposition. Silver Spring, MD: Association for Information and Image Management International, 1998.

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Kataja, Kari J. Numerical modelling of near field optical data storage. [Espoo, Finland]: VTT Technical Research Centre of Finland, 2005.

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Bertino, Elisa. Indexing Techniques for Advanced Database Systems. Boston, MA: Springer US, 1997.

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Bearman, David. Archives & museum data models & dictionaries. Pittsburgh, Pa: Archives & Museum Informatics, 1990.

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Bearman, David. Archives & museum data models & dictionaries. Pittsburgh: Archives & Museum Informatics, 1990.

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8

Candan, K. Selçuk. Data management for multimedia retrieval. Cambridge: Cambridge University Press, 2010.

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Ferrigno, Carmelo F. A data-management system for detailed areal interpretive data. Denver, Colo: U.S. Dept. of the Interior, Geological Survey, 1986.

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Ferrigno, Carmelo F. A data-management system for detailed areal interpretive data. Denver, Colo: U.S. Dept. of the Interior, Geological Survey, 1986.

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Capítulos de livros sobre o assunto "Data models, storage and indexing"

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Feng, Dan. "Indexing Schemes". In Data Deduplication for High Performance Storage System, 53–68. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0112-6_4.

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Harrison, Guy. "Data Models and Storage". In Next Generation Databases, 145–66. Berkeley, CA: Apress, 2015. http://dx.doi.org/10.1007/978-1-4842-1329-2_10.

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Grefenstette, Gregory, e Laura Wilber. "Data Models & Storage". In Search-Based Applications, 23–37. Cham: Springer International Publishing, 2011. http://dx.doi.org/10.1007/978-3-031-02274-6_4.

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Gibbons, Philip B. "Data Storage and Indexing in Sensor Networks". In Encyclopedia of Database Systems, 635–38. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_112.

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Gibbons, Phillip B. "Data Storage and Indexing in Sensor Networks". In Encyclopedia of Database Systems, 1–4. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_112-2.

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Gibbons, Phillip B. "Data Storage and Indexing in Sensor Networks". In Encyclopedia of Database Systems, 850–53. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_112.

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Wu, Dongyao, Sherif Sakr e Liming Zhu. "Big Data Storage and Data Models". In Handbook of Big Data Technologies, 3–29. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-49340-4_1.

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Kumari, Poonam, Amit Kr Mishra e Vivek Sharma. "A Survey: Secure Indexing & Storage of Big Data". In Conference Proceedings of ICDLAIR2019, 37–45. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67187-7_5.

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Schneider, Ralf. "Storage and Indexing of Fine Grain, Large Scale Data Sets". In Sustained Simulation Performance 2013, 89–104. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01439-5_7.

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Palm, Günther, e Friedrich T. Sommer. "Associative Data Storage and Retrieval in Neural Networks". In Models of Neural Networks III, 79–118. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-0723-8_3.

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Trabalhos de conferências sobre o assunto "Data models, storage and indexing"

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Guo, Yang, e Zili Shao. "Cymo: A Storage Model with Query-Aware Indexing for Spatio-Temporal Big Data". In 2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS). IEEE, 2022. http://dx.doi.org/10.1109/icdcs54860.2022.00021.

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Yi, Michael, Kamil Ceglinski, Pradeepkumar Ashok, Michael Behounek, Spencer White, Trey Peroyea e Taylor Thetford. "Applications of Large Language Models in Well Construction Planning and Real-Time Operation". In IADC/SPE International Drilling Conference and Exhibition. SPE, 2024. http://dx.doi.org/10.2118/217700-ms.

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Abstract In today's well construction operations, a substantial volume of data is generated and stored across multiple databases. The primary objective being to use them as a guide for future well construction optimization. However, much of this data gets lost in computer storage, and appropriate information is difficult to find at the right time. This paper shows the results of deploying a generative pre-trained transformer (GPT) large language model on an operator's dataset to alleviate this problem. The process starts with gathering all relevant data into a common database. In this case, the dataset included sensor data, processed data, morning reports, end of well reports, after-action reviews of non-productive times, bit forensics data and publicly available data from wells drilled by other operators. The files were pre-processed, and metadata was added appropriately to ensure appropriate indexing and training of the information. This data is then fed to the cloud platform on which the model is learnt. The model is then integrated into the data platform so that the end users can pose queries. The dataset consisted of more than 200 wells of the operator in a region that the operator is actively drilling. Data curation was a time-consuming task that had to be performed to ensure only quality and organized information was fed to the model. Documents containing well construction related subject matter were also used in the training to provide the end user assistance with core concepts. During the test stage, a multitude of questions were posed to the platform, including questions such as: What happened the last time there was a stuck pipe in this region? What is the best ROP that could be attained in the lateral section? Significant time savings were recorded due to the ease with which information could be retrieved. A big concern was the potential for wrongs answers being provided to the questions. To alleviate this concern, all answers were accompanied by references found in the database, to give the person reviewing the answers confidence in the answers. This paper introduces the benefits that large language models (LLMs) bring to both well planning and real-time operations. LLM offers the capability to be able to retrieve information extremely quickly and provide answers in a conversational format to user questions. This paper also provides recommendations to the industry and details some of the challenges to adopting LLMs.
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Tishechkin, Dmitriy, e Yannick Agbor. "Step into Geological Samples Digital Twins". In ADIPEC. SPE, 2023. http://dx.doi.org/10.2118/216868-ms.

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Abstract Energy industry digitalization is emerging in greater importance due to higher demands to understand and characterize the Earth’s surface and subsurface structure. This urge drives the need to improve efficiency of conventional processes in data acquisition, rock modeling and data management workflow associated with it. The recent advancements in the technology for data acquisition, information technology, cloud, and high-performance computing in both software and hardware have positively impacted our ability to collect, store, classify, manage and process rock data. In this paper, we aim to demonstrate a streamlined workflow from field data acquisition to characterization of rock samples acquired by variety of ways. Acquisition itself made a leap in recent years. Today, difficult to reach outcrop samples can be acquired with Unmanned Aerial Vehicles (UAV) (Carlos Alberto Villarreal, 2022) such as drones, quadcopters, etc. In combination with LiDAR technology, it became possible to acquire three dimensional models of outcrop rock structures in higher resolution and use them with 3D geomodelling and structural modeling software solutions. A typical oil and gas company, would acquire many thousands of images of geological samples via variety of ways, including LIDAR, UAV, drilling cores via laboratory equipment, etc. All these images are stored at the acquisition site or at the scattered storage facilities around the world, making it hard to access, manipulate, process and collaborate. Traditionally, if images need to be accessed, it takes a long time to procure them to the asset team. We will address a centralized ingesting, storage, indexing data platform that facilitates data as asset methodology to treat data just like an important company asset. One of the ways to consume and contribute to the geological samples data platform is to enrich data contained in the platform. In oil and gas, deep understanding of flow and transport phenomena in rocks is essential to help asset managers, geoscientists, geologists, and reservoir engineers to optimize production. The availability of X-ray microtomography and high-resolution tomographic images of rock samples, produced by photogrammetry and LiDARs, make it possible to study the flow and transport phenomena at the pore scale, allowing for non-destructive assessment of different scenarios which would not possible with existing laboratory experimental methods. Additionally, traditional lab tests are costly, time-consuming, and inconsistent, presenting uncertainties and risks to key business decisions. We will demonstrate how to speed up the processing with highly parallel methodology and open-source software (OpenFOAM) running in the cloud’s virtually unlimited compute and storage.
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Panwar, Ajeet Pal Singh, Devendra Kumar e Jaydesh Chandra. "Hierarchal data storage using NAI indexing". In 2017 3rd International Conference on Advances in Computing,Communication & Automation (ICACCA) (Fall). IEEE, 2017. http://dx.doi.org/10.1109/icaccaf.2017.8344687.

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Alsubaiee, Sattam, Michael J. Carey e Chen Li. "LSM-Based Storage and Indexing". In SIGMOD/PODS'15: International Conference on Management of Data. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2786006.2786007.

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Shraer, Alexander. "Session details: Industry 2: Storage & Indexing". In SIGMOD/PODS '19: International Conference on Management of Data. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3341283.

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Yujie Liu, Xiaodong Zhang, Yang Liu e Zongmin Li. "3D models’ indexing and compression storage in digital library". In 2008 IEEE International Symposium on IT in Medicine and Education (ITME). IEEE, 2008. http://dx.doi.org/10.1109/itme.2008.4743888.

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Weintraub, Grisha, Ehud Gudes e Shlomi Dolev. "Indexing cloud data lakes within the lakes". In SYSTOR '21: The 14th ACM International Systems and Storage Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3456727.3463828.

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Binnig, Carsten. "Session details: Research session 19: storage and indexing". In SIGMOD/PODS'14: International Conference on Management of Data. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/3255769.

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Pereira, Anil L. "Parallel Data Indexing and Storage on a COTS Cluster". In 2020 International Conference on Computational Science and Computational Intelligence (CSCI). IEEE, 2020. http://dx.doi.org/10.1109/csci51800.2020.00236.

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Relatórios de organizações sobre o assunto "Data models, storage and indexing"

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Bethel, E. Wes, Luke Gosink, John Shalf, Kurt Stockinger e Kesheng Wu. HDF5-FastQuery: An API for Simplifying Access to Data Storage,Retrieval, Indexing and Querying. Office of Scientific and Technical Information (OSTI), junho de 2006. http://dx.doi.org/10.2172/888964.

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Artemov, S. N., e V. Krupski. Efficient Models for Data Storage with References. Fort Belvoir, VA: Defense Technical Information Center, julho de 1997. http://dx.doi.org/10.21236/ada344311.

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Stevens, Rick. Development of an Extensible Computational Framework for Centralized Storage and Distributed Curation and Analysis of Genomic Data Genome-scale Metabolic Models. Office of Scientific and Technical Information (OSTI), agosto de 2010. http://dx.doi.org/10.2172/1234257.

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García-Espinosa, J., e C. Soriano. Data management plan. Scipedia, 2021. http://dx.doi.org/10.23967/prodphd.2021.9.003.

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This document presents the deliverable D8.1 – the Data Management Plan (DMP) of work package 8 of the prodPhD project. It aims to present the plan for the management, generation, collection, security, preservation and sharing of data generated through the prodPhD project. The DMP is a key element for organizing the project’s data. It provides an analysis of the data, which will be collected, processed and published by the prodPhD consortium. The project embraces the initiatives of the European Commission to promote the open access to research data, aiming to improve and maximize access to and reuse of research data generated by Horizon 2020 projects. In this sense prodPhD will adhere to the Open Research Data Pilot (ORD Pilot) fostered by the European Commission, and this DMP will be developed following the standards of data storage, access and management. This plan will detail what data will be generated through the project, whether and how it will be made accessible for the verification and reuse and how it will be curated and preserved. In this context, the term data applies to the information generated during the different experimental campaigns carried out in the project, and specifically to the data, including associated metadata, to be used to validate the computational models and the technical solutions to be developed in the project. This document is the first version of the DMP and may be updated throughout the project, if significant changes (new data, changes in consortium policies, changes in consortium composition, etc.) arise.
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Fertitta, David, Maxwell Agnew e David Ramirez. Technical assessment of the Old, Mississippi, Atchafalaya, and Red (OMAR) Rivers : HEC-RAS model. Engineer Research and Development Center (U.S.), agosto de 2022. http://dx.doi.org/10.21079/11681/45170.

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Upstream of the confluence of the Red River, Atchafalaya River, and ORCC Outflow Channel are vast low-lying flat areas on both sides of the Lower Red River. During times of high water on the Lower Red—whether from upstream water in the Red or from the ORCC Outflow Channel—enormous amounts of water flow over the natural riverbanks and flood this land. The loss of this water from the river into storage affects the operation of the ORCC, which in turn affects the stages and flows down the Atchafalaya and Mississippi Rivers. An improved understanding of this area and how water is stored during flood events is required to inform ORCC water management operations. Hydraulic analyses provide a basis to assess the changes in water levels, current directions and velocities, and flow rates for the assessment area. The hydraulic model HEC-RAS is used to expand on existing models of the area and to help overcome gaps in data. Understanding the processes of how water leaves the Red River channel, the volume and timing of the water moving into storage, and when the storage area begins to drain, will greatly inform the water managers and operators of the ORCC.
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Semerikov, Serhiy, Illia Teplytskyi, Yuliia Yechkalo, Oksana Markova, Vladimir Soloviev e Arnold Kiv. Computer Simulation of Neural Networks Using Spreadsheets: Dr. Anderson, Welcome Back. [б. в.], junho de 2019. http://dx.doi.org/10.31812/123456789/3178.

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The authors of the given article continue the series presented by the 2018 paper “Computer Simulation of Neural Networks Using Spreadsheets: The Dawn of the Age of Camelot”. This time, they consider mathematical informatics as the basis of higher engineering education fundamentalization. Mathematical informatics deals with smart simulation, information security, long-term data storage and big data management, artificial intelligence systems, etc. The authors suggest studying basic principles of mathematical informatics by applying cloud-oriented means of various levels including those traditionally considered supplementary – spreadsheets. The article considers ways of building neural network models in cloud-oriented spreadsheets, Google Sheets. The model is based on the problem of classifying multi-dimensional data provided in “The Use of Multiple Measurements in Taxonomic Problems” by R. A. Fisher. Edgar Anderson’s role in collecting and preparing the data in the 1920s-1930s is discussed as well as some peculiarities of data selection. There are presented data on the method of multi-dimensional data presentation in the form of an ideograph developed by Anderson and considered one of the first efficient ways of data visualization.
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Olsen e Wilson. L52145 Geomechanical Analysis and Design Considerations for Thin-Bedded Salt Caverns. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), dezembro de 2005. http://dx.doi.org/10.55274/r0011349.

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he bedded salt formations located throughout the United States are layered and interspersed with non-salt materials such as anhydrite, shale, dolomite and limestone. The salt layers often contain significant impurities. GRI and DOE have initialized this research proposal in order to increase the gas storage capabilities by providing operators with improved geotechnical design and operating guidelines for thin bedded salt caverns. Terralog has summarized the geologic conditions, pressure conditions and critical design factors that may lead to: Fracture in heterogeneous materials; Differential deformation and bedding plane slip; Propagation of damage around single and multiple cavern; Improved design recommendations for single and multiple cavern configurations in various bedded salt environments. The existing caverns within both the Permian Basin Complex and the Michigan and Appalachian Basins are normally found between 300 m to 1,000 m (1,000 ft to 3,300 ft) depth depending on local geology and salt dissolution depth. Currently, active cavern operations are found in the Midland and Anadarko Basins within the Permian Basin Complex and in the Appalachian and Michigan Basins. The Palo Duro and Delaware Basins within the Permian Basin Complex also offer salt cavern development potential. Terralog developed a number of numerical models for caverns located in thin bedded salt. A modified creep viscoplastic model has been developed and implemented in Flac3D to simulate the response of salt at the Permian, Michigan and Appalachian Basins. The formulation of the viscoplastic salt model, which is based on an empirical creep law developed for Waste Isolation Pilot Plant (WIPP) Program, is combined with the Drucker-Prager model to include the formation of damage and failure. The Permian salt lab test data provided by Pfeifle et al. 1983, are used to validate the assumptions made in the material model development. For the actual cavern simulations two baseline models are developed for single and multiple caverns, respectively. Different parameters that affect damage propagation and deformation of salt cavern, such as cavern pressure, operating conditions, cavern height/diameter ratio, overburden stiffness and roof thickness are analyzed and the respective results summarized. For multiple horizontal caverns numerical models are developed to determine the cavern interaction and the minimum safe center to center distance. A step by step methodology for operators to assess critical cavern design parameters for thin bedded salt formations is also presented.
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Bédard, K., A. Marsh, M. Hillier e Y. Music. 3D geological model of the Western Canadian Sedimentary Basin in Saskatchewan, Canada. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/331747.

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The Western Canadian Sedimentary Basin (WCSB) covers a large part of southern Saskatchewan and hosts many resources such as critical mineral deposits (i.e. potash, helium and lithium) as well as oil and gas reservoirs and is also targeted for deep CO2 storage projects. There is also growing interest in the groundwater resources, the geothermal potential and hydrogen recovery potential. These applications require knowledge of the subsurface geology such as formation thickness and depth, relationships with adjacent formations or unconformities and ultimately, distribution of physical properties within the basin. 3D geological models can provide this knowledge since they characterize the geometry of subsurface geological features. In addition, they can be used as a framework for fluid flow simulation and estimating the distribution a variety of properties. The 3D geological model presented in this report consists of 51 geological units of which, 49 are stratigraphic units spanning from Cambrian Deadwood Formation at the base of the sequence to Upper Cretaceous Belly River Formation at the top, plus the undivided Precambrian and a preliminary Quaternary unit. The model is cut by 7 major regional unconformities, including the base of the Quaternary sediments. The regional model was constrained using oil and gas well data interpretations, provincial scale bedrock geology maps and knowledge from the previously interpreted areal extent of the Phanerozoic strata. A hybrid explicit-implicit modelling approach was employed to produce the 3D geological model of the WCSB in Saskatchewan using Gocad/SKUATM geomodelling software.
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Rudd, Ian. Leveraging Artificial Intelligence and Robotics to Improve Mental Health. Intellectual Archive, julho de 2022. http://dx.doi.org/10.32370/iaj.2710.

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Artificial Intelligence (AI) is one of the oldest fields of computer science used in building structures that look like human beings in terms of thinking, learning, solving problems, and decision making (Jovanovic et al., 2021). AI technologies and techniques have been in application in various aspects to aid in solving problems and performing tasks more reliably, efficiently, and effectively than what would happen without their use. These technologies have also been reshaping the health sector's field, particularly digital tools and medical robotics (Dantas & Nogaroli, 2021). The new reality has been feasible since there has been exponential growth in the patient health data collected globally. The different technological approaches are revolutionizing medical sciences into dataintensive sciences (Dantas & Nogaroli, 2021). Notably, with digitizing medical records supported the increasing cloud storage, the health sector created a vast and potentially immeasurable volume of biomedical data necessary for implementing robotics and AI. Despite the notable use of AI in healthcare sectors such as dermatology and radiology, its use in psychological healthcare has neem models. Considering the increased mortality and morbidity levels among patients with psychiatric illnesses and the debilitating shortage of psychological healthcare workers, there is a vital requirement for AI and robotics to help in identifying high-risk persons and providing measures that avert and treat mental disorders (Lee et al., 2021). This discussion is focused on understanding how AI and robotics could be employed in improving mental health in the human community. The continued success of this technology in other healthcare fields demonstrates that it could also be used in redefining mental sicknesses objectively, identifying them at a prodromal phase, personalizing the treatments, and empowering patients in their care programs.
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The Integrated Economic-Environmental Modeling Platform: IEEM Platform Technical Guides: The Ecosystem Services Modeling Data Packet: Overview and Guidelines for Use. Inter-American Development Bank, março de 2021. http://dx.doi.org/10.18235/0003076.

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This Technical Note describes the ecosystem service model data packets which were developed through the Integrated Economic-Environmental Modeling (IEEM) Platform project to facilitate the application of ecosystem services modeling to support evidence-based public policy and investment decision making. The data packets provide the spatial data and lookup tables needed to run the InVEST carbon storage, annual water yield, sediment delivery ratio, and nutrient delivery ratio models for 21 countries (and counting) in the Latin American and the Caribbean region. This Technical Note describes the content and structure of the data packets, model specific considerations, the alignment of land cover data for use in InVEST lookup tables, the customization of model parameters, and best practices in the application of the data packets.
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