Relevant bibliographies by topics / Data-Intensive Systems

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Data-Intensive Systems'

Author: Grafiati
Published: 11 December 2022
Last updated: 25 January 2023

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Journal articles on the topic "Data-Intensive Systems"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Data-Intensive Systems"

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Xu, Yiqi. "Storage Management of Data-intensive Computing Systems." FIU Digital Commons, 2016. http://digitalcommons.fiu.edu/etd/2474.

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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 storage management is critical to application performance in such data-intensive computing systems. However, existing resource management frameworks in these systems lack the support for storage management, which causes unpredictable performance degradations when applications are under I/O contention. Storage management of data-intensive systems is a challenging problem because I/O resources cannot be easily partitioned and distributed storage systems require scalable management. This dissertation presents the solutions to address these challenges for typical data-intensive systems including high-performance computing (HPC) systems and big-data systems. For HPC systems, the dissertation presents vPFS, a performance virtualization layer for parallel file system (PFS) based storage systems. It employs user-level PFS proxies to interpose and schedule parallel I/Os on a per-application basis. Based on this framework, it enables SFQ(D)+, a new proportional-share scheduling algorithm which allows diverse applications with good performance isolation and resource utilization. To manage an HPC system’s total I/O service, it also provides two complementary synchronization schemes to coordinate the scheduling of large numbers of storage nodes in a scalable manner. For big-data systems, the dissertation presents IBIS, an interposition-based big-data I/O scheduler. By interposing the different I/O phases of big-data applications, it schedules the I/Os transparently to the applications. It enables a new proportional-share scheduling algorithm, SFQ(D2), to address the dynamics of the underlying storage by adaptively adjusting the I/O concurrency. Moreover, it employs a scalable broker to coordinate the distributed I/O schedulers and provide proportional sharing of a big-data system’s total I/O service. Experimental evaluations show that these solutions have low-overhead and provide strong I/O performance isolation. For example, vPFS’ overhead is less than 3% in through- put and it delivers proportional sharing within 96% of the target for diverse workloads; and IBIS provides up to 99% better performance isolation for WordCount and 30% better proportional slowdown for TeraSort and TeraGen than native YARN.
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Cai, Simin. "Systematic Design of Data Management for Real-Time Data-Intensive Applications." Licentiate thesis, Mälardalens högskola, Inbyggda system, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-35369.

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Modern real-time data-intensive systems generate large amounts of data that are processed using complex data-related computations such as data aggregation. In order to maintain the consistency of data, such computations must be both logically correct (producing correct and consistent results) and temporally correct (completing before specified deadlines). One solution to ensure logical and temporal correctness is to model these computations as transactions and manage them using a Real-Time Database Management System (RTDBMS). Ideally, depending on the particular system, the transactions are customized with the desired logical and temporal correctness properties, which are achieved by the customized RTDBMS with appropriate run-time mechanisms. However, developing such a data management solution with provided guarantees is not easy, partly due to inadequate support for systematic analysis during the design. Firstly, designers do not have means to identify the characteristics of the computations, especially data aggregation, and to reason about their implications. Design flaws might not be discovered, and thus they may be propagated to the implementation. Secondly, trade-off analysis of conflicting properties, such as conflicts between transaction isolation and temporal correctness, is mainly performed ad-hoc, which increases the risk of unpredictable behavior. In this thesis, we propose a systematic approach to develop transaction-based data management with data aggregation support for real-time systems. Our approach includes the following contributions: (i) a taxonomy of data aggregation, (ii) a process for customizing transaction models and RTDBMS, and (iii) a pattern-based method of modeling transactions in the timed automata framework, which we show how to verify with respect to transaction isolation and temporal correctness. Our proposed taxonomy of data aggregation processes helps in identifying their common and variable characteristics, based on which their implications can be reasoned about. Our proposed process allows designers to derive transaction models with desired properties for the data-related computations from system requirements, and decide the appropriate run-time mechanisms for the customized RTDBMS to achieve the desired properties. To perform systematic trade-off analysis between transaction isolation and temporal correctness specifically, we propose a method to create formal models of transactions with concurrency control, based on which the isolation and temporal correctness properties can be verified by model checking, using the UPPAAL tool. By applying the proposed approach to the development of an industrial demonstrator, we validate the applicability of our approach.
DAGGERS
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Schnell, Felicia. "Multicast Communication for Increased Data Exchange in Data- Intensive Distributed Systems." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-232132.

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Modern applications are required to handle and communicate an increasing amount of data. Meanwhile, distributed systems containing multiple computationally weak components becomes more common, resulting in a problematic situation. Choosing among communication strategies, used for delivering message between entities, therefore becomes crucial in order to efficiently utilize available resources. Systems where identical data is delivered to many recipients are common nowadays, but may apply an underlying communication strategy based on direct interaction between sender and receiver which is insufficient. Multicasting refers to a technique for group communication where messages can be distributed to participating nodes in a single transmission. This technique is developed to circumvent the problem of high workload on sender side and redundant traffic in the network, and constitutes the focus for this thesis. Within the area of Electronic Warfare and self-protection systems, time constitutes a critical aspect in order to provide relevant information for decision making. Self-protection systems developed by Saab, used in military aircrafts, must provide situational awareness to guarantee that correct decisions can be made at the right time. With more advanced systems, where the amount of data needed to be transmitted increases, the need of fast communication is essential to achieve quality of service. This thesis investigates how the deployment of multicast, in a distributed data-intensive system, could prepare a system for increased data exchange. The result is a communication design which allows for the system to distribute messages to a group of receivers with less effort from the sender and with reduced redundant traffic transferred over the same link. Comparative measurements are conducted between the new implementation and the old system. The result of the evaluation shows that the multicast solution both can decrease the time for message handling as well as the workload on endpoints significantly.
Nutidens applikationer måste kunna hantera och kommunicera en ökad datamängd. Samtidigt har distribuerade system bestående av många beräkningsmässigt svaga enheter blivit allt mer vanligt, vilket är problematiskt. Valet av kommunikationsstrategi, för att leverera data mellan enheter i ett system, är därför av stor betydelse för att uppnå effektivt utnyttjande av tillgängliga resurser. System där identisk information ska distribueras till flertalet mottagare är vanligt förekommande idag. Den underliggande kommunikationsstrategin som används kan dock baseras på direkt interaktion mellan sändare och mottagare vilket är ineffektivt. Multicast (Flersändning) syftar till ett samlingsbegrepp inom datorkommunikation baserat på gruppsändning av information. Denna teknik är utvecklad för att kringgå problematiken med hög belastning på sändarsidan och dessutom minska belastningen på nätverket, och utgör fokus för detta arbete. Inom telekrigföring och självskyddssystem utgör tiden en betydande faktor för att kunna tillhandahålla relevant information som kan stödja beslutsfattning. För självskyddssystem utvecklade av Saab, vilka används i militärflygplan, är situationsmedvetenhet av stor betydelse då det möjliggör för att korrekta beslut kan tas vid rätt tidpunkt. Genom utvecklingen av mer avancerade system, där mängden meddelanden som måste passera genom nätverket ökar, tillkommer höga krav på snabb kommunikation för att kunna åstadkomma kvalité. Denna uppsatsrapport undersöker hur införandet av multicast, i ett dataintensivt distribuerat system, kan förbereda ett system för ökat datautbyte. Arbetet har resulterat i en kommunikationsdesign som gör det möjligt för systemet att distribuera meddelanden till grupp av mottagare med minskad belastning på sändarsidan och mindre redundant trafik på de utgående länkarna. Jämförandet mätningar har gjorts mellan den nya implementationen och det gamla systemet. Resultaten visar att multicast-lösningen både kan reducera tiden för meddelande hantering samt belastningen på ändnoder avsevärt.
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Yeom, Jae-seung. "Optimizing Data Accesses for Scaling Data-intensive Scientific Applications." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/64180.

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Data-intensive scientific applications often process an enormous amount of data. The scalability of such applications depends critically on how to manage the locality of data. Our study explores two common types of applications that are vastly different in terms of memory access pattern and workload variation. One includes those with multi-stride accesses in regular nested parallel loops. The other is for processing large-scale irregular social network graphs. In the former case, the memory location or the data item accessed in a loop is predictable and the load on processing a unit work (an array element) is relatively uniform with no significant variation. On the other hand, in the latter case, the data access per unit work (a vertex) is highly irregular in terms of the number of accesses and the locations being accessed. This property is further tied to the load and presents significant challenges in the scalability of the application performance. Designing platforms to support extreme performance scaling requires understanding of how application specific information can be used to control the locality and improve the performance. Such insights are necessary to determine which control and which abstraction to provide for interfacing an underlying system and an application as well as for designing a new system. Our goal is to expose common requirements of data-intensive scientific applications for scalability. For the former type of applications, those with regular accesses and uniform workload, we contribute new methods to improve the temporal locality of software-managed local memories, and optimize the critical path of scheduling data transfers for multi-dimensional arrays in nested loops. In particular, we provide a runtime framework allowing transparent optimization by source-to-source compilers or automatic fine tuning by programmers. Finally, we demonstrate the effectiveness of the approach by comparing against a state-of-the-art language-based framework. For the latter type, those with irregular accesses and non-uniform workload, we analyze how the heavy-tailed property of input graphs limits the scalability of the application. Then, we introduce an application-specific workload model as well as a decomposition method that allows us to optimize locality with the custom load balancing constraints of the application. Finally, we demonstrate unprecedented strong scaling of a contagion simulation on two state-of-the-art high performance computing platforms.
Ph. D.
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Khemiri, Wael. "Data-intensive interactive workflows for visual analytics." Phd thesis, Université Paris Sud - Paris XI, 2011. http://tel.archives-ouvertes.fr/tel-00659227.

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The increasing amounts of electronic data of all forms, produced by humans (e.g. Web pages, structured content such as Wikipedia or the blogosphere etc.) and/or automatic tools (loggers, sensors, Web services, scientific programs or analysis tools etc.) leads to a situation of unprecedented potential for extracting new knowledge, finding new correlations, or simply making sense of the data.Visual analytics aims at combining interactive data visualization with data analysis tasks. Given the explosion in volume and complexity of scientific data, e.g., associated to biological or physical processes or social networks, visual analytics is called to play an important role in scientific data management.Most visual analytics platforms, however, are memory-based, and are therefore limited in the volume of data handled. Moreover, the integration of each new algorithm (e.g. for clustering) requires integrating it by hand into the platform. Finally, they lack the capability to define and deploy well-structured processes where users with different roles interact in a coordinated way sharing the same data and possibly the same visualizations.This work is at the convergence of three research areas: information visualization, database query processing and optimization, and workflow modeling. It provides two main contributions: (i) We propose a generic architecture for deploying a visual analytics platform on top of a database management system (DBMS) (ii) We show how to propagate data changes to the DBMS and visualizations, through the workflow process. Our approach has been implemented in a prototype called EdiFlow, and validated through several applications. It clearly demonstrates that visual analytics applications can benefit from robust storage and automatic process deployment provided by the DBMS while obtaining good performance and thus it provides scalability.Conversely, it could also be integrated into a data-intensive scientific workflow platform in order to increase its visualization features.
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Vijayakumar, Sruthi. "Hadoop Based Data Intensive Computation on IAAS Cloud Platforms." UNF Digital Commons, 2015. http://digitalcommons.unf.edu/etd/567.

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Cloud computing is a relatively new form of computing which uses virtualized resources. It is dynamically scalable and is often provided as pay for use service over the Internet or Intranet or both. With increasing demand for data storage in the cloud, the study of data-intensive applications is becoming a primary focus. Data intensive applications are those which involve high CPU usage, processing large volumes of data typically in size of hundreds of gigabytes, terabytes or petabytes. The research in this thesis is focused on the Amazon’s Elastic Cloud Compute (EC2) and Amazon Elastic Map Reduce (EMR) using HiBench Hadoop Benchmark suite. HiBench is a Hadoop benchmark suite and is used for performing and evaluating Hadoop based data intensive computation on both these cloud platforms. Both quantitative and qualitative comparisons of Amazon EC2 and Amazon EMR are presented. Also presented are their pricing models and suggestions for future research.
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Maheshwari, Ketan. "Data-intensive scientific workflows : representations of parallelism and enactment on distributed systems." Nice, 2011. http://www.theses.fr/2011NICE4007.

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Le portage d'applications manipulant de grandes masses de données sur des infrastructures de calcul distribué à grande échelle est un problème difficile. Combler l'écart entre l'application et sa description sous forme de workflow soulève des défis fia différents niveaux. Le défi au niveau de l'utilisateur final est le besoin d'exprimer la logique de l'application et des dépendances de flots de données dans un domaine non-technique. Au niveau de l'infrastructure, il s'agit d'un défi pour le portage de l'application sur infrastructures fia grande échelle en optimisant l'exploitation des ressources distribuées. Les workflows permettent le déploiement d'applications distribuées grâce fia la représentation formelle de composants les constituant, de leurs interactions et des flots de données véhiculés. Cependant, la description de workflows et leurs gestionnaires d'exécution nécessitent des améliorations pour relever les défis mentionnés. Faciliter la description du parallélisme sous une forme concise, la combinaison des données et des structures de données de haut niveau de manière cohérente est nécessaire. Cette thèse vise fia satisfaire ces exigences. Partant du cas d'utilisation de traitement d'images médicales, plusieurs stratégies sont développées afin d'exprimer le parallélisme et l'exécution asynchrone de worflkows complexes en fournissant une expression concise et un gestionnaire d'exécution interfacé avec des infrastructures fia grande échelle. Les contributions principales de cette thèse sont: a) Un langage riche de workflows disposant de deux représentations. L'exécution des applications de traitement d'images médicales décrites avec ce langage sur la grille de calcul européenne (EGI) donne des résultats expérimentaux fructueux. B) Une extension d'un environnement d'exécution existant de flots applicatifs (Taverna) pour permettre l'exécution de l'application sur les infrastructures fia grande échelle
Porting data-intensive applications on large scale distributed computing infrastructures is not trivial. Bridging the gap between application and its workflow expression poses challenges at different levels. The challenge at the end-user level is a need to express the application's logic and data flow requirements from a non-technical domain. At the infrastructure level, it is a challenge to port the application such that a maximum exploitation of the underlying resources can takes place. Workflows enable distributed application deployment by recognizing the application component's inter-connections and the flow among them. However, workflow expressions and engines need enhancements to meet the challenges outlined. Facilitation of a concise expression of parallelism, data combinations and higher level data structures in a coherent fashion is required. This work targets to fulfill these requirements. It is driven by the use-cases in the field of medical image processing domain. Various strategies are developed to efficiently express asynchronous and maximum parallel execution of complex flows by providing concise expression and enactments interfaced with large scale distributed computing infrastructures. The main contributions of this research are: a) A rich workflow language with two-way expression and fruitful results from the experiments carried out on enactment of medical image processing applications workflows on the European Grid Computing Infrastructure; and b) Extension of an existing workflow environment (Taverna) to interface with the Grid Computing Infrastructures
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Schäler, Martin [Verfasser], and Gunter [Akademischer Betreuer] Saake. "Minimal-invasive provenance integration into data-intensive systems / Martin Schäler. Betreuer: Gunter Saake." Magdeburg : Universitätsbibliothek, 2014. http://d-nb.info/1066295352/34.

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Shang, Pengju. "Research in high performance and low power computer systems for data-intensive environment." Doctoral diss., University of Central Florida, 2011. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5033.

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According to the data affinity, DAFA re-organizes data to maximize the parallelism of the affinitive data, and also subjective to the overall load balance. This enables DAFA to realize the maximum number of map tasks with data-locality. Besides the system performance, power consumption is another important concern of current computer systems. In the U.S. alone, the energy used by servers which could be saved comes to 3.17 million tons of carbon dioxide, or 580,678 cars {Kar09}. However, the goals of high performance and low energy consumption are at odds with each other. An ideal power management strategy should be able to dynamically respond to the change (either linear or nonlinear, or non-model) of workloads and system configuration without violating the performance requirement. We propose a novel power management scheme called MAR (modeless, adaptive, rule-based) in multiprocessor systems to minimize the CPU power consumption under performance constraints. By using richer feedback factors, e.g. the I/O wait, MAR is able to accurately describe the relationships among core frequencies, performance and power consumption. We adopt a modeless control model to reduce the complexity of system modeling. MAR is designed for CMP (Chip Multi Processor) systems by employing multi-input/multi-output (MIMO) theory and per-core level DVFS (Dynamic Voltage and Frequency Scaling).; TRAID deduplicates this overlap by only logging one compact version (XOR results) of recovery references for the updating data. It minimizes the amount of log content as well as the log flushing overhead, thereby boosts the overall transaction processing performance. At the same time, TRAID guarantees comparable RAID reliability, the same recovery correctness and ACID semantics of traditional transactional processing systems. On the other hand, the emerging myriad data intensive applications place a demand for high-performance computing resources with massive storage. Academia and industry pioneers have been developing big data parallel computing frameworks and large-scale distributed file systems (DFS) widely used to facilitate the high-performance runs of data-intensive applications, such as bio-informatics {Sch09}, astronomy {RSG10}, and high-energy physics {LGC06}. Our recent work {SMW10} reported that data distribution in DFS can significantly affect the efficiency of data processing and hence the overall application performance. This is especially true for those with sophisticated access patterns. For example, Yahoo's Hadoop {refg} clusters employs a random data placement strategy for load balance and simplicity {reff}. This allows the MapReduce {DG08} programs to access all the data (without or not distinguishing interest locality) at full parallelism. Our work focuses on Hadoop systems. We observed that the data distribution is one of the most important factors that affect the parallel programming performance. However, the default Hadoop adopts random data distribution strategy, which does not consider the data semantics, specifically, data affinity. We propose a Data-Affinity-Aware (DAFA) data placement scheme to address the above problem. DAFA builds a history data access graph to exploit the data affinity.; The evolution of computer science and engineering is always motivated by the requirements for better performance, power efficiency, security, user interface (UI), etc {CM02}. The first two factors are potential tradeoffs: better performance usually requires better hardware, e.g., the CPUs with larger number of transistors, the disks with higher rotation speed; however, the increasing number of transistors on the single die or chip reveals super-linear growth in CPU power consumption {FAA08a}, and the change in disk rotation speed has a quadratic effect on disk power consumption {GSK03}. We propose three new systematic approaches as shown in Figure 1.1, Transactional RAID, data-affinity-aware data placement DAFA and Modeless power management, to tackle the performance problem in Database systems, large scale clusters or cloud platforms, and the power management problem in Chip Multi Processors, respectively. The first design, Transactional RAID (TRAID), is motivated by the fact that in recent years, more storage system applications have employed transaction processing techniques Figure 1.1 Research Work Overview] to ensure data integrity and consistency. In transaction processing systems(TPS), log is a kind of redundancy to ensure transaction ACID (atomicity, consistency, isolation, durability) properties and data recoverability. Furthermore, high reliable storage systems, such as redundant array of inexpensive disks (RAID), are widely used as the underlying storage system for Databases to guarantee system reliability and availability with high I/O performance. However, the Databases and storage systems tend to implement their independent fault tolerant mechanisms {GR93, Tho05} from their own perspectives and thereby leading to potential high overhead. We observe the overlapped redundancies between the TPS and RAID systems, and propose a novel reliable storage architecture called Transactional RAID (TRAID).
ID: 030423445; System requirements: World Wide Web browser and PDF reader.; Mode of access: World Wide Web.; Thesis (Ph.D.)--University of Central Florida, 2011.; Includes bibliographical references (p. 119-128).
Ph.D.
Doctorate
Electrical Engineering and Computer Science
Engineering and Computer Science
Computer Science
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Saito, Yasushi. "Functionally homogeneous clustering : a framework for building scalable data-intensive internet services /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/6936.

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Books on the topic "Data-Intensive Systems"

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Wiktorski, Tomasz. Data-intensive Systems. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04603-3.

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Armando, Escalante, and SpringerLink (Online service), eds. Handbook of Data Intensive Computing. New York, NY: Springer Science+Business Media, LLC, 2011.

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The fourth paradigm: Data-intensive scientific discovery. Redmond, Wash: Microsoft Research, 2009.

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Data-intensive computing: Architectures, algorithms, and applications. Cambridge: Cambridge University Press, 2013.

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1979-, Pan Jie, and Teng Fei, eds. Cloud-computing: Data-intensive computing and scheduling. Boca Raton: CRC Press, 2012.

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Data intensive distributed computing: Challenges and solutions for large-scale information management. Hershey, PA: Information Science Reference, 2012.

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International, Workshop on User Interfaces to Data Intensive Systems (2nd 2001 Zurich Switzerland). User Interfaces to Data Intensive Systems: Proceedings : Second International Workshop on User Interfaces to Data Intensive Systems : UIDIS 2001 : 31 May-1 June, 2001, Zurich, Switzerland. Los Alamitos, California: IEEE Computer Society, 2001.

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International, Workshop on Interfaces to Database Systems (1999 Edinburgh Scotland). User Interfaces to Data Intensive Systems: 5-6 September 1999, Edinburgh, Scotland : proceedings. Los Alamitos, Calif: IEEE Computer Society, 1999.

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IEEE Symposium on Mass Storage Systems. (8th 1987 Tucson, Ariz.). Digest of papers: Eight IEEE Symposium on Mass Storage Systems : emerging solutions for data-intensive applications, May 11-14, 1987, Doubletree Hotel, Tuscon, Arizona. Edited by Friedman Karen, IEEE Computer Society, and Institute of Electrical and Electronics Engineers. Washington, D.C: IEEE Computer Society Press, 1987.

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European Research Consortium for Informatics and Mathematics and Association for Computing Machinery. Special Interest Group on Human-Computer Interaction., eds. Proceedings: Second International Workshop on User Interfaces to Data Intensive Systems : UIDIS 2001 : 31 May-1 June 2001, Zurich, Switzerland ; sponsored by European Research Consortium on Informatics and Mathematics, Association of Computing Machinery Special Interest Group on Human-Computer Interaction. Los Alamitos, California: IEEE Computer Society, 2001.

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Book chapters on the topic "Data-Intensive Systems"

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Leonelli, Sabina. "Data-Intensive Research." In Encyclopedia of Systems Biology, 545–48. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_58.

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Guida, Giovanni, Gianfranco Lamperti, and Marina Zanella. "Data and Knowledge Intensive Systems." In Software Prototyping in Data and Knowledge Engineering, 73–98. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-011-4267-0_4.

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Mattmann, Chris A., Daniel J. Crichton, Andrew F. Hart, Cameron Goodale, J. Steven Hughes, Sean Kelly, Luca Cinquini, et al. "Architecting Data-Intensive Software Systems." In Handbook of Data Intensive Computing, 25–57. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1415-5_2.

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Balali, Farhad, Jessie Nouri, Adel Nasiri, and Tian Zhao. "Implementation Tools of IoT Systems." In Data Intensive Industrial Asset Management, 159–203. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-35930-0_9.

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Galvão, João, Ana Leon, Carlos Costa, Maribel Yasmina Santos, and Óscar Pastor López. "Automating Data Integration in Adaptive and Data-Intensive Information Systems." In Information Systems, 20–34. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63396-7_2.

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Fernandez, Eduardo B. "Security in Data Intensive Computing Systems." In Handbook of Data Intensive Computing, 447–66. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1415-5_16.

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Felderer, Michael, Barbara Russo, and Florian Auer. "On Testing Data-Intensive Software Systems." In Security and Quality in Cyber-Physical Systems Engineering, 129–48. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-25312-7_6.

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Schmidt, Joachim W., and Winfried Lamersdorf. "Programming Support for Data-Intensive Applications." In Pictorial Information Systems in Medicine, 263–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 1986. http://dx.doi.org/10.1007/978-3-642-82384-8_9.

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Distefano, Salvatore, and Antonio Puliafito. "Information Security in Large Scale Distributed Systems." In Handbook of Data Intensive Computing, 485–500. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1415-5_18.

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Horváth, Zoltán, Zoltán Hernyák, Tamás Kozsik, Máté Tejfel, and Attila Ulbert. "A Data Intensive Computation on a Cluster." In Distributed and Parallel Systems, 46–53. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-1167-0_6.

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Conference papers on the topic "Data-Intensive Systems"

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Emanuilov, Simeon, and Aleksandar Dimov. "Column-oriented data model for data-intensive systems." In 2022 10th International Scientific Conference on Computer Science (COMSCI). IEEE, 2022. http://dx.doi.org/10.1109/comsci55378.2022.9912610.

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"Proceedings User Interfaces to Data Intensive Systems." In Proceedings User Interfaces to Data Intensive Systems. IEEE, 1999. http://dx.doi.org/10.1109/uidis.1999.791456.

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del Campo, Fernando Martin, and Paul Chow. "Architecture Exploration for Data Intensive Applications." In MEMSYS '15: International Symposium on Memory Systems. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2818950.2818970.

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Cuzzocrea, Alfredo, and Edoardo Fadda. "Data-Intensive Object-Oriented Adaptive Web Systems." In MEDES '20: 12th International Conference on Management of Digital EcoSystems. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3415958.3433051.

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Foidl, Harald, Michael Felderer, and Stefan Biffl. "Technical Debt in Data-Intensive Software Systems." In 2019 45th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). IEEE, 2019. http://dx.doi.org/10.1109/seaa.2019.00058.

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Vanderdonckt, J. "Advice-giving systems for selecting interaction objects." In Proceedings User Interfaces to Data Intensive Systems. IEEE, 1999. http://dx.doi.org/10.1109/uidis.1999.791471.

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"DYNAMICALLY RECONFIGURABLE DATA-INTENSIVE SERVICE COMPOSITION." In 6th International Conference on Web Information Systems and Technologies. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0002852101250130.

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Keim, D. A., E. E. Koutsofios, and S. C. North. "Visual exploration of large telecommunication data sets." In Proceedings User Interfaces to Data Intensive Systems. IEEE, 1999. http://dx.doi.org/10.1109/uidis.1999.791458.

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Andrienko, G. L., and N. V. Andrienko. "Data mining with C4.5 and interactive cartographic visualization." In Proceedings User Interfaces to Data Intensive Systems. IEEE, 1999. http://dx.doi.org/10.1109/uidis.1999.791473.

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Dix, A. "Design of user interfaces for the Web." In Proceedings User Interfaces to Data Intensive Systems. IEEE, 1999. http://dx.doi.org/10.1109/uidis.1999.791457.

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Reports on the topic "Data-Intensive Systems"

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Musmanno, Joseph. Data Intensive Systems (DIS) Benchmark Performance Summary. Fort Belvoir, VA: Defense Technical Information Center, August 2003. http://dx.doi.org/10.21236/ada418752.

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Boehm, Barry. Software Intensive Systems Data Quality and Estimation Research in Support of Future Defense Cost Analysis. Fort Belvoir, VA: Defense Technical Information Center, March 2012. http://dx.doi.org/10.21236/ada608488.

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Boehm, Barry. RT 6 - Software Intensive Systems Data Quality and Estimation Research in Support of Future Defense Cost Analysis. Fort Belvoir, VA: Defense Technical Information Center, March 2012. http://dx.doi.org/10.21236/ada582892.

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Rangaswami, Raju. Department of Energy Project ER25739 Final Report QoS-Enabled, High-performance Storage Systems for Data-Intensive Scientific Computing. Office of Scientific and Technical Information (OSTI), May 2009. http://dx.doi.org/10.2172/1046919.

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Author, Not Given. Active system area networks for data intensive computations. Final report. Office of Scientific and Technical Information (OSTI), April 2002. http://dx.doi.org/10.2172/771294.

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Cook, Samantha, Marissa Torres, Nathan Lamie, Lee Perren, Scott Slone, and Bonnie Jones. Automated ground-penetrating-radar post-processing software in R programming. Engineer Research and Development Center (U.S.), September 2022. http://dx.doi.org/10.21079/11681/45621.

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Ground-penetrating radar (GPR) is a nondestructive geophysical technique used to create images of the subsurface. A major limitation of GPR is that a subject matter expert (SME) needs to post-process and interpret the data, limiting the technique’s use. Post-processing is time-intensive and, for detailed processing, requires proprietary software. The goal of this study is to develop automated GPR post-processing software, compatible with Geophysical Survey Systems, Inc. (GSSI) data, in open-source R programming. This would eliminate the need for an SME to process GPR data, remove proprietary software dependencies, and render GPR more accessible. This study collected GPR profiles by using a GSSI SIR4000 control unit, a 100 MHz antenna, and a Trimble GPS. A standardized method for post-processing data was then established, which includes static data removal, time-zero correction, distance normalization, data filtering, and stacking. These steps were scripted and automated in R programming, excluding data filtering, which was used from an existing package, RGPR. The study compared profiles processed using GSSI software to profiles processed using the R script developed here to ensure comparable functionality and output. While an SME is currently still necessary for interpretations, this script eliminates the need for one to post-process GSSI GPR data.
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Lasko, Kristofer, and Sean Griffin. Monitoring Ecological Restoration with Imagery Tools (MERIT) : Python-based decision support tools integrated into ArcGIS for satellite and UAS image processing, analysis, and classification. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40262.

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Monitoring the impacts of ecosystem restoration strategies requires both short-term and long-term land surface monitoring. The combined use of unmanned aerial systems (UAS) and satellite imagery enable effective landscape and natural resource management. However, processing, analyzing, and creating derivative imagery products can be time consuming, manually intensive, and cost prohibitive. In order to provide fast, accurate, and standardized UAS and satellite imagery processing, we have developed a suite of easy-to-use tools integrated into the graphical user interface (GUI) of ArcMap and ArcGIS Pro as well as open-source solutions using NodeOpenDroneMap. We built the Monitoring Ecological Restoration with Imagery Tools (MERIT) using Python and leveraging third-party libraries and open-source software capabilities typically unavailable within ArcGIS. MERIT will save US Army Corps of Engineers (USACE) districts significant time in data acquisition, processing, and analysis by allowing a user to move from image acquisition and preprocessing to a final output for decision-making with one application. Although we designed MERIT for use in wetlands research, many tools have regional or global relevancy for a variety of environmental monitoring initiatives.
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Suir, Glenn, Christina Saltus, Charles Sasser, J. Harris, Molly Reif, Rodrigo Diaz, and Gabe Giffin. Evaluating drone truthing as an alternative to ground truthing : an example with wetland plant identification. Engineer Research and Development Center (U.S.), October 2021. http://dx.doi.org/10.21079/11681/42201.

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Satellite remote sensing of wetlands provides many advantages to traditional monitoring and mapping methods. However, remote sensing often remains reliant on labor- and resource- intensive ground truth data for wetland vegetation identification through image classification training and accuracy assessments. Therefore, this study sought to evaluate the use of unmanned aircraft system (UAS) data as an alternative or supplement to traditional ground truthing techniques in support of remote sensing for identifying and mapping wetland vegetation.
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Suir, Glenn, Christina Saltus, Charles Sasser, J. Harris, Molly Reif, Rodrigo Diaz, and Gabe Giffin. Evaluating drone truthing as an alternative to ground truthing : an example with wetland plant identification. Engineer Research and Development Center (U.S.), October 2021. http://dx.doi.org/10.21079/11681/42201.

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Satellite remote sensing of wetlands provides many advantages to traditional monitoring and mapping methods. However, remote sensing often remains reliant on labor- and resource- intensive ground truth data for wetland vegetation identification through image classification training and accuracy assessments. Therefore, this study sought to evaluate the use of unmanned aircraft system (UAS) data as an alternative or supplement to traditional ground truthing techniques in support of remote sensing for identifying and mapping wetland vegetation.
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Chefetz, Benny, and Jon Chorover. Sorption and Mobility of Pharmaceutical Compounds in Soils Irrigated with Treated Wastewater. United States Department of Agriculture, 2006. http://dx.doi.org/10.32747/2006.7592117.bard.

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Research into the fate of pharmaceutical compounds (PCs) in the environment has focused on aspects of removal efficiency during sewage treatment, degradation in surface water and accumulation in soils and sediments. However, very little information is available on the binding interactions of pharmaceuticals with dissolved organic matter (DOM) originating from wastewater treatment. Such interactions can significantly affect the transport potential of PCs in soils by altering compound affinity for soil particle surfaces. Our primary hypothesis is that the transport potential of PCs in soils is strongly impacted by the type and strength of interaction with DOM and the stability of resulting DOM-PC complexes. The overarching goal of the proposed work is to develop a better understanding of the risk associated with introduction of PCs into the environment with treated wastewater. This goal has been achieved by elucidating the mechanisms of the interaction of selected pharmaceuticals (that have shown to be widespread wastewater contaminants) with DOM constituents; by determining the stability and fate of DOM-PC complexes introduced to soils and soil constituents; and by evaluating the potential uptake of these compounds by plants. Based on the results obtained in this study (column and batch sorption-desorption experiments), we suggest that PCs can be classified as slow-mobile compounds in SOM-rich soil layers. When these compounds pass this layer and/or are introduced into SOM-poor soils, their mobility increases significantly. Our data suggest that in semiarid soils (consisting of low SOM), PCs can potentially be transported to the groundwater in fields irrigated with reclaimed wastewater. Moreover, the higher mobility of the acid PCs (i.e., naproxen and diclofenac) in freshwater column systems suggests that their residues in soils irrigated with reclaimed wastewater can leach from the root zone and be transported to the groundwater after rain events. Our data obtained from the binding experiments of PCs with DOM demonstrate that the hydrophobic DOM fractions were more efficient at sorbing PCs than the more polar hydrophilic fractions at a pH near the pKa of the analytes. At the pH of natural semiarid water and soil systems, including that of reclaimed wastewater and biosolids, the role of the hydrophobic fractions as sorption domains is less important than the contribution of the hydrophilic fractions. We also hypothesize that the DOM fractions interact with each other at the molecular level and do not act as independent sorption domains. In summary, our data collected in the BARD project demonstrate that the sorption abilities of the DOM fractions can also significantly affect the mobility of pharmaceutical compounds in soils influenced by intensive irrigation with treated wastewater or amended with biosolids.
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