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Journal articles on the topic 'Data-Intensive Systems'

Author: Grafiati

Published: 11 December 2022

Last updated: 25 January 2023

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1

Shah, Mehul A., Michael J. Franklin, Samuel Madden, and Joseph M. Hellerstein. "Java support for data-intensive systems." ACM SIGMOD Record 30, no. 4 (December 2001): 103–14. http://dx.doi.org/10.1145/604264.604282.

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2

Tuzhilin, Alexander, and Zvi M. Kedem. "Modeling data-intensive reactive systems with relational transition systems." Acta Informatica 33, no. 3 (May 1996): 203–31. http://dx.doi.org/10.1007/s002360050041.

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3

Kamel, N. N. "Predicate caching for data-intensive autonomous systems." Computer 30, no. 11 (1997): 77–83. http://dx.doi.org/10.1109/2.634867.

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4

TAN, H. B. K. "Sizing Data-Intensive Systems from ER Model." IEICE Transactions on Information and Systems E89-D, no. 4 (April 1, 2006): 1321–26. http://dx.doi.org/10.1093/ietisy/e89-d.4.1321.

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5

Sellis, T., C. Lin, and L. Raschid. "Data intensive production systems: the DIPS approach." ACM SIGMOD Record 18, no. 3 (September 1989): 52–58. http://dx.doi.org/10.1145/71031.71038.

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6

Zdravković, Milan, and Ricardo Jardim-Gonçalves. "Model-driven data-intensive Enterprise Information Systems." Enterprise Information Systems 12, no. 8-9 (October 4, 2018): 910–14. http://dx.doi.org/10.1080/17517575.2018.1526327.

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7

Quinn, A., P. Ettler, L. Jirsa, I. Nagy, and P. Nedoma. "Probabilistic advisory systems for data-intensive applications." International Journal of Adaptive Control and Signal Processing 17, no. 2 (2003): 133–48. http://dx.doi.org/10.1002/acs.743.

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8

Maier, David, Lois Delcambre, Calton Pu, Jon Walpole, Goetz Graefe, and Len Shapiro. "Database research at the Data-Intensive Systems Center." ACM SIGMOD Record 22, no. 4 (December 1993): 81–86. http://dx.doi.org/10.1145/166635.166661.

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9

Roth, Philip C., and R. Shane Canon. "Special Issue on Data-Intensive Scalable Computing Systems." Parallel Computing 61 (January 2017): 1–2. http://dx.doi.org/10.1016/j.parco.2017.01.001.

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10

Metnitz, Ph G. H., M. Hiesmayr, C. Popow, and K. Lenz. "Patient Data Management Systems in Intensive Care — 1996." International Journal of Clinical Monitoring and Computing 13, no. 2 (May 1996): 99–102. http://dx.doi.org/10.1007/bf02915846.

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11

Ouhammou, Yassine, Ladjel Bellatreche, Mirjana Ivanovic, and Alberto Abelló. "Model and data engineering for advanced data-intensive systems and applications." Computing 101, no. 10 (July 23, 2019): 1391–95. http://dx.doi.org/10.1007/s00607-019-00726-3.

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12

Horn, Werner. "AI in medicine on its way from knowledge-intensive to data-intensive systems." Artificial Intelligence in Medicine 23, no. 1 (August 2001): 5–12. http://dx.doi.org/10.1016/s0933-3657(01)00072-0.

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13

Ramon, Jan, Daan Fierens, Fabián Güiza, Geert Meyfroidt, Hendrik Blockeel, Maurice Bruynooghe, and Greet Van Den Berghe. "Mining data from intensive care patients." Advanced Engineering Informatics 21, no. 3 (July 2007): 243–56. http://dx.doi.org/10.1016/j.aei.2006.12.002.

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14

Buza, Antal. "The bounds of the distributed data-intensive computing systems." Pollack Periodica 2, Supplement 1 (December 2007): 85–96. http://dx.doi.org/10.1556/pollack.2.2007.s.8.

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15

Alexander, W., and G. Copeland. "Process and dataflow control in distributed data-intensive systems." ACM SIGMOD Record 17, no. 3 (June 1988): 90–98. http://dx.doi.org/10.1145/971701.50212.

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16

Gunning, K., and K. Rowan. "ABC of intensive care: Outcome data and scoring systems." BMJ 319, no. 7204 (July 24, 1999): 241–44. http://dx.doi.org/10.1136/bmj.319.7204.241.

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17

König-Ries, Brigitta, and Peter C. Lockermann. "Research in databases and data-intensive applications." ACM SIGMOD Record 26, no. 3 (September 1997): 67–72. http://dx.doi.org/10.1145/262762.262771.

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18

Ceri, Stefano, Piero Fraternali, and Stefano Paraboschi. "Design principles for data-intensive Web sites." ACM SIGMOD Record 28, no. 1 (March 1999): 84–89. http://dx.doi.org/10.1145/309844.310064.

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19

Jenn-Wei Lin, Chien-Hung Chen, and J. Morris Chang. "QoS-Aware Data Replication for Data-Intensive Applications in Cloud Computing Systems." IEEE Transactions on Cloud Computing 1, no. 1 (January 2013): 101–15. http://dx.doi.org/10.1109/tcc.2013.1.

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20

Ma, Hui, Klaus-Dieter Schewe, Bernhard Thalheim, and Qing Wang. "A theory of data-intensive software services." Service Oriented Computing and Applications 3, no. 4 (November 17, 2009): 263–83. http://dx.doi.org/10.1007/s11761-009-0051-x.

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21

Ding, Jie, Hai Yun Han, and Ai Hua Zhou. "A Data Placement Strategy for Data-Intensive Cloud Storage." Advanced Materials Research 354-355 (October 2011): 896–900. http://dx.doi.org/10.4028/www.scientific.net/amr.354-355.896.

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Data-Intensive applications in power systems often perform complex computations which always involve large amount of datasets. In a distributed environment, an application may needs several datasets located in different data centers which faces two challenges including the high cost of data movements between data centers and data dependencies within the same data centers. In this paper, a data placement strategy among and within data centers in a cloud environment is proposed. Datasets are placed in different centers by a clustering scheme based on the data dependencies. And within the center, data is partitioned and replicated using consistent hashing. Simulations show that the algorithm can effectively reduce the cost of data movements and perform a evenly data distribution.

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22

Hwang, Seung-won. "Report on data-intensive software management and mining." ACM SIGMOD Record 40, no. 1 (July 18, 2011): 32–34. http://dx.doi.org/10.1145/2007206.2007216.

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23

Raymond, Renita, and S. Margret Anouncia. "Identification of Data-Intensive Systems Requirements using Semantic Similarity Search." Journal of Engineering Science and Technology Review 15, no. 2 (2022): 215–27. http://dx.doi.org/10.25103/jestr.152.25.

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24

Savarimuthu, Margret Anouncia, and Renita Raymond. "Identification of Data-Intensive Systems Requirements using Semantic Similarity Search." Journal of Engineering Science and Technology Review 15, no. 1 (2022): 199–211. http://dx.doi.org/10.25103/jestr.151.25.

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25

Marney, Steven, and Mamdouh Ibrahim. "Using objects to manage in-memory data intensive expert systems." ACM SIGPLAN OOPS Messenger 6, no. 4 (October 1995): 67–71. http://dx.doi.org/10.1145/260111.260237.

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26

Nguyen, Khanh, Kai Wang, Yingyi Bu, Lu Fang, and Guoqing Xu. "Understanding and Combating Memory Bloat in Managed Data-Intensive Systems." ACM Transactions on Software Engineering and Methodology 26, no. 4 (February 23, 2018): 1–41. http://dx.doi.org/10.1145/3162626.

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27

Alexander, W., and G. Copeland. "Comparison of dataflow control techniques in distributed data-intensive systems." ACM SIGMETRICS Performance Evaluation Review 16, no. 1 (May 1988): 157–66. http://dx.doi.org/10.1145/1007771.55614.

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28

Ghafarian, Toktam, and Bahman Javadi. "Cloud-aware data intensive workflow scheduling on volunteer computing systems." Future Generation Computer Systems 51 (October 2015): 87–97. http://dx.doi.org/10.1016/j.future.2014.11.007.

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29

Subramanian, Ranjini, and Hui Zhang. "Automatic code parallelization for data-intensive computing in multicore systems." Journal of Physics: Conference Series 1411 (November 2019): 012014. http://dx.doi.org/10.1088/1742-6596/1411/1/012014.

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30

Guo, Qing, Xiaochen Guo, Yuxin Bai, Ravi Patel, Engin Ipek, and Eby G. Friedman. "Resistive Ternary Content Addressable Memory Systems for Data-Intensive Computing." IEEE Micro 35, no. 5 (September 2015): 62–71. http://dx.doi.org/10.1109/mm.2015.89.

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31

Zanoni, Marco, Fabrizio Perin, Francesca Arcelli Fontana, and Gianluigi Viscusi. "Pattern detection for conceptual schema recovery in data-intensive systems." Journal of Software: Evolution and Process 26, no. 12 (July 14, 2014): 1172–92. http://dx.doi.org/10.1002/smr.1656.

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32

Castrén, A., S. Grimnes, A. Kari, P. Nikki, G. L. Olsson, H. B. Rasmussen, E. D. Sivak, E. Vauramo, and B. Zarén. "User requirements for data systems in anaesthesia and intensive care." International Journal of Clinical Monitoring and Computing 5, no. 3 (September 1988): 137–46. http://dx.doi.org/10.1007/bf02933709.

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33

Kołodziej, Joanna, Martin Gilje Jaatun, Samee Ullah Khan, and Mario Koeppen. "“Security-Aware and Data Intensive Low-Cost Mobile Systems” Editorial." Mobile Networks and Applications 18, no. 5 (October 2013): 591–93. http://dx.doi.org/10.1007/s11036-013-0474-7.

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34

Lederer, Matthias, and Joanna Riedl. "Data Science Techniques in Knowledge-Intensive Business Processes." International Journal of Data Analytics 1, no. 1 (January 2020): 52–67. http://dx.doi.org/10.4018/ijda.2020010104.

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The processes of an investment bank are considered to be particularly knowledge-intensive, because analysts need to extract or generate relevant knowledge from a variety of data. With increasing digitization, modern data science and business intelligence techniques are available to support or partially automate these activities. This study presents concrete use cases for front office processes of an investment bank as how knowledge management techniques can be used. For example, the article describes how expert systems can be used in the due diligence review or how fuzzy logic systems help in deciding whether to buy or sell securities. The article is based on 1079 texts (e.g. documented cases and articles) and serves researchers as well as practitioners as an application overview of data science techniques in the example area of knowledge-intensive banking processes.

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35

Li, Rundong, Ningfang Mi, Mirek Riedewald, Yizhou Sun, and Yi Yao. "Abstract cost models for distributed data-intensive computations." Distributed and Parallel Databases 37, no. 3 (August 24, 2018): 411–39. http://dx.doi.org/10.1007/s10619-018-7244-2.

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36

Savarimuthu, Margret Anouncia, and Renita Raymond. "Transformation Requirement Pattern for Capturing Data-Intensive Applications Requirements." Journal of Internet Services and Information Security 12, no. 4 (November 30, 2022): 126–38. http://dx.doi.org/10.58346/jisis.2022.i4.009.

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A business analyst typically encounters activities and features that they have previously worked on as they move from project to project. Interestingly, despite the fact that these tasks are identical, analysts typically begin to work on them from scratch. Recreating the wheel leads to fundamental problems like neglecting the flaws in requirement analysis. Therefore, the analyst must begin implementing the strategy of requirement reuse through requirements patterns to ensure higher quality requirements with far less ambiguous parts and in a brief time. The focus of building a requirement pattern for transformation requirements of software-intensive systems ensures that critical information is not overlooked. It also enhances the effectiveness of business analysts. Although there is a plethora of research on requirements patterns in the studies, the studies did not focus on the requirement patterns for software-intensive systems. This research paper focuses mainly on the interactions of data-intensive systems namely transition requirements and a Transformation Requirement Pattern (TFReqPat) template is generated to capture the transitions requirements. It also analyses the anatomy of TFReqPat which is a guideline for capturing the data-intensive applications transition requirements. As a case study, a requirement pattern catalog with a systematic example of requirements for a banking application is presented. Furthermore, an empirical investigation was conducted to study participant’s perceptions of requirement patterns in general and in particular in order to evaluate the proposed TFReqPat. The statistical analysis reveals that the TFReqPat is more appropriate, efficient, and successful for software-intensive systems than the conventional reuse strategy.

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37

Zhang, Wangda, Junyoung Kim, Kenneth A. Ross, Eric Sedlar, and Lukas Stadler. "Adaptive code generation for data-intensive analytics." Proceedings of the VLDB Endowment 14, no. 6 (February 2021): 929–42. http://dx.doi.org/10.14778/3447689.3447697.

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Modern database management systems employ sophisticated query optimization techniques that enable the generation of efficient plans for queries over very large data sets. A variety of other applications also process large data sets, but cannot leverage database-style query optimization for their code. We therefore identify an opportunity to enhance an open-source programming language compiler with database-style query optimization. Our system dynamically generates execution plans at query time, and runs those plans on chunks of data at a time. Based on feedback from earlier chunks, alternative plans might be used for later chunks. The compiler extension could be used for a variety of data-intensive applications, allowing all of them to benefit from this class of performance optimizations.

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38

Duan, Jiguang, Yan Li, Liying Duan, and Amit Sharma. "Time Effective Cloud Resource Scheduling Method for Data-Intensive Smart Systems." International Journal of Information Technology and Web Engineering 17, no. 1 (January 1, 2022): 1–15. http://dx.doi.org/10.4018/ijitwe.306915.

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The cloud computing platforms are being deployed nowadays for resource scheduling of real time data intensive applications. Cloud computing still deals with the challenge of time oriented effective scheduling for resource allocation, while striving to provide the efficient quality of service. This article proposes a time prioritization-based ensemble resource management and Ant Colony based optimization (ERM-ACO) algorithm in order to aid effective resource allocation and scheduling mechanism which specifically deals with the task group feasibility, assessing and selecting the computing and the storage resources required to perform specific tasks. The research outcomes are obtained in terms of time-effective demand fulfillment rate, average response time as well as resource utilization time considering various grouping mechanisms based on data arrival intensity consideration. The proposed framework when compared to the present state-of-the-art methods, optimal fitness percentage of 98% is observed signifying the feasible outcomes for real-time scenarios.

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39

Moore, Philip T., and Hai V. Pham. "Personalization and rule strategies in data-intensive intelligent context-aware systems." Knowledge Engineering Review 30, no. 2 (March 2015): 140–56. http://dx.doi.org/10.1017/s0269888914000265.

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AbstractThe concept of personalization in its many forms has gained traction driven by the demands of computer-mediated interactions generally implemented in large-scale distributed systems and ad hoc wireless networks. Personalization requires the identification and selection of entities based on a defined profile (a context); an entity has been defined as a person, place, or physical or computational object. Context employs contextual information that combines to describe an entities current state. Historically, the range of contextual information utilized (in context-aware systems) has been limited to identity, location, and proximate data; there has, however, been advances in the range of data and information addressed. As such, context can be highly dynamic with inherent complexity. In addition, context-aware systems must accommodate constraint satisfaction and preference compliance.This article addresses personalization and context with consideration of the domains and systems to which context has been applied and the nature of the contextual data. The developments in computing and service provision are addressed with consideration of the relationship between the evolving computing landscape and context. There is a discussion around rule strategies and conditional relationships in decision support. Logic systems are addressed with an overview of the open world assumption versus the closed world assumption and the relationship with the Semantic Web. The event-driven rule-based approach, which forms the basis upon which intelligent context processing can be realized, is presented with an evaluation and proof-of-concept. The issues and challenges identified in the research are considered with potential solutions and research directions; alternative approaches to context processing are discussed. The article closes with conclusions and open research questions.

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40

Metnitz, P. G. H., and K. Lenz. "Patient data management systems in intensive care — the situation in Europe." Intensive Care Medicine 21, no. 9 (September 1995): 703–15. http://dx.doi.org/10.1007/bf01704737.

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41

de Keizer, N. F., C. P. Stoutenbeek, L. A. J. B. W. Hanneman, and E. de Jonge. "An evaluation of patient data management systems in dutch intensive care." Intensive Care Medicine 24, no. 2 (February 1998): 167–71. http://dx.doi.org/10.1007/s001340050540.

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42

Segall, Richard S., Jeffrey S. Cook, and Gao Niu. "Overview of Big Data-Intensive Storage and its Technologies for Cloud and Fog Computing." International Journal of Fog Computing 2, no. 1 (January 2019): 74–113. http://dx.doi.org/10.4018/ijfc.2019010104.

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Computing systems are becoming increasingly data-intensive because of the explosion of data and the needs for processing the data, and subsequently storage management is critical to application performance in such data-intensive computing systems. However, if existing resource management frameworks in these systems lack the support for storage management, this would cause unpredictable performance degradation when applications are under input/output (I/O) contention. Storage management of data-intensive systems is a challenge. Big Data plays a most major role in storage systems for data-intensive computing. This article deals with these difficulties along with discussion of High Performance Computing (HPC) systems, background for storage systems for data-intensive applications, storage patterns and storage mechanisms for Big Data, the Top 10 Cloud Storage Systems for data-intensive computing in today's world, and the interface between Big Data Intensive Storage and Cloud/Fog Computing. Big Data storage and its server statistics and usage distributions for the Top 500 Supercomputers in the world are also presented graphically and discussed as data-intensive storage components that can be interfaced with Fog-to-cloud interactions and enabling protocols.

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43

Ahmad, Yanif, Randal Burns, Michael Kazhdan, Charles Meneveau, Alex Szalay, and Andreas Terzis. "Scientific data management at the Johns Hopkins institute for data intensive engineering and science." ACM SIGMOD Record 39, no. 3 (February 8, 2011): 18–23. http://dx.doi.org/10.1145/1942776.1942782.

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44

Berente, Nicholas, Stefan Seidel, and Hani Safadi. "Research Commentary—Data-Driven Computationally Intensive Theory Development." Information Systems Research 30, no. 1 (March 2019): 50–64. http://dx.doi.org/10.1287/isre.2018.0774.

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45

Miceli, Christopher, Michael Miceli, Bety Rodriguez-Milla, and Shantenu Jha. "Understanding performance of distributed data-intensive applications." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 368, no. 1926 (September 13, 2010): 4089–102. http://dx.doi.org/10.1098/rsta.2010.0168.

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Grids, clouds and cloud-like infrastructures are capable of supporting a broad range of data-intensive applications. There are interesting and unique performance issues that appear as the volume of data and degree of distribution increases. New scalable data-placement and management techniques, as well as novel approaches to determine the relative placement of data and computational workload, are required. We develop and study a genome sequence matching application that is simple to control and deploy, yet serves as a prototype of a data-intensive application. The application uses a SAGA-based implementation of the All-Pairs pattern. This paper aims to understand some of the factors that influence the performance of this application and the interplay of those factors. We also demonstrate how the SAGA approach can enable data-intensive applications to be extensible and interoperable over a range of infrastructure. This capability enables us to compare and contrast two different approaches for executing distributed data-intensive applications—simple application-level data-placement heuristics versus distributed file systems.

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46

Sukhoroslov, Oleg. "Toward efficient execution of data-intensive workflows." Journal of Supercomputing 77, no. 8 (January 12, 2021): 7989–8012. http://dx.doi.org/10.1007/s11227-020-03612-4.

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47

Jovanovic, Petar, Oscar Romero, Alkis Simitsis, and Alberto Abello. "Incremental Consolidation of Data-Intensive Multi-Flows." IEEE Transactions on Knowledge and Data Engineering 28, no. 5 (May 1, 2016): 1203–16. http://dx.doi.org/10.1109/tkde.2016.2515609.

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48

Siozios, Kostas, and Dimitrios Soudris. "Designing a novel high-performance FPGA architecture for data intensive applications." Journal of Real-Time Image Processing 4, no. 2 (October 7, 2008): 155–66. http://dx.doi.org/10.1007/s11554-008-0099-4.

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49

Malik, Saif Ur Rehman, Samee U. Khan, Sam J. Ewen, Nikos Tziritas, Joanna Kolodziej, Albert Y. Zomaya, Sajjad A. Madani, et al. "Performance analysis of data intensive cloud systems based on data management and replication: a survey." Distributed and Parallel Databases 34, no. 2 (March 14, 2015): 179–215. http://dx.doi.org/10.1007/s10619-015-7173-2.

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50

Wang, Tao, Shihong Yao, Zhengquan Xu, and Shan Jia. "DCCP: an effective data placement strategy for data-intensive computations in distributed cloud computing systems." Journal of Supercomputing 72, no. 7 (August 30, 2015): 2537–64. http://dx.doi.org/10.1007/s11227-015-1511-z.

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