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Journal articles on the topic 'Data Stream Management System'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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1

Jovanovic, Zeljko. "Data stream management system for moving sensor object data." Serbian Journal of Electrical Engineering 12, no. 1 (2015): 117–27. http://dx.doi.org/10.2298/sjee1501117j.

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Sensor and communication development has led to the development of new types of applications. Classic database data storage becomes inadequate when data streams arrive from multiple sensors. Then, data querying and result presentation are not efficient. The desired results are obtained with a delay, and the database is filled with a large amount of unnecessary data. To adequately support the above applications, Data Stream Management System (DSMS) applications are needed. DSMSs provide real-time data stream processing. In this paper, a client-server system is presented with DSMS realized on the Java WebDSMS application server side. WebDSMS functionalities are tested with simulated data and in real-life usage.

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2

Maison, Rafal, and Maciej Zakrzewicz. "Content-based load shedding in multimedia data stream management system." Foundations of Computing and Decision Sciences 37, no. 2 (October 1, 2012): 79–95. http://dx.doi.org/10.2478/v10209-011-0007-8.

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Abstract.Overload management has become very important in public safety systems that analyse high performance multimedia data streams, especially in the case of detection of terrorist and criminal dangers. Efficient overload management improves the accuracy of automatic identification of persons suspected of terrorist or criminal activity without requiring interaction with them. We argue that in order to improve the quality of multimedia data stream processing in the public safety arena, the innovative concept of a Multimedia Data Stream Management System (MMDSMS) using load-shedding techniques should be introduced into the infrastructure to monitor and optimize the execution of multimedia data stream queries. In this paper, we present a novel content-centered load shedding framework, based on searching and matching algorithms, for analysing video tuples arriving within multimedia data streams. The framework tracks and registers all symptoms of overload, and either prevents overload before it occurs, or minimizes its effects. We have extended our Continuous Query Language (CQL) syntax to enable this load shedding technique. The effectiveness of the framework has been verified using both artificial and real data video streams collected from monitoring devices.

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Shanmugam, D. B., C. Karthi, S. Bharath Babu, and S. Munusamy. "Data Stream Clustering Challenges and Management System." Journal of Computational and Theoretical Nanoscience 16, no. 5 (May 1, 2019): 2393–97. http://dx.doi.org/10.1166/jctn.2019.7906.

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DEZFULI, MOHAMMAD G., and MOSTAFA S. HAGHJOO. "PROBABILISTIC QUERYING OVER UNCERTAIN DATA STREAMS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 20, no. 05 (October 2012): 701–28. http://dx.doi.org/10.1142/s0218488512500328.

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Inherent imprecision of data in many applications motivates us to support uncertainty as a first-class concept. Data stream and probabilistic data have been recently considered noticeably in isolation. However, there are many applications including sensor data management systems and object monitoring systems which need both issues in tandem. Our main contribution is designing a probabilistic data stream management system, called Sarcheshmeh, for continuous querying over probabilistic data streams. Sarcheshmeh supports uncertainty from input data to final query results. In this paper, after reviewing requirements and applications of probabilistic data streams, we present our new data model for probabilistic data streams and define our main logical operators formally. Then, we present query language and physical operators. In addition, we introduce the architecture of Sarcheshmeh and also describe some major challenges like memory management and our floating precision mechanism toward designing a more robust system. Finally, we report evaluation of our system and the effect of floating precision on the tradeoff between accuracy and efficiency.

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Li, Yue-jie. "Data Stream of Wireless Sensor Networks Based on Deep Learning." International Journal of Online Engineering (iJOE) 12, no. 11 (November 24, 2016): 22. http://dx.doi.org/10.3991/ijoe.v12i11.6232.

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The sensor data in wireless sensor networks are continuously arriving in multiple, rapid, time varying, possibly unpredictable, unbounded streams, and no record of historical information is kept. These limitations make conventional Database Management Systems and their evolution unsuitable for streams. Thereby there is a need to build a complete Data Streaming Management System (DSMS), which could process streams and perform dynamic continuous query processing. In this paper, a framework for Adaptive Distributed Data Streaming Management System (ADDSMS) is presented, which operates as streams control interface between arrays of distributed data stream sources and end-user clients who access and analyze these streams. Simulation results show that the proposed method can thus improve overall system performance substantially.

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Mohammadi, Shirin, Ali A. Safaei, Fatemeh Abdi, and Mostafa S. Haghjoo. "Adaptive Data Stream Management System Using Learning Automata." Advanced Computing: An International Journal 2, no. 5 (September 30, 2011): 1–14. http://dx.doi.org/10.5121/acij.2011.2501.

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Golab, Lukasz, and M. Tamer Özsu. "Issues in data stream management." ACM SIGMOD Record 32, no. 2 (June 2003): 5–14. http://dx.doi.org/10.1145/776985.776986.

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8

Lin, Edgar Chia Han. "Research on Sequence Query Processing Techniques over Data Streams." Applied Mechanics and Materials 284-287 (January 2013): 3507–11. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3507.

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Due to the great progress of computer technology and mature development of network, more and more data are generated and distributed through the network, which is called data streams. During the last couple of years, a number of researchers have paid their attention to data stream management, which is different from the conventional database management. At present, the new type of data management system, called data stream management system (DSMS), has become one of the most popular research areas in data engineering field. Lots of research projects have made great progress in this area. Since the current DSMS does not support queries on sequence data, this project will study the issues related to two types of data. First, we will focus on the content filtering on single-attribute streams, such as sensor data. Second, we will focus on multi-attribute streams, such as video films. We will discuss the related issues such as how to build an efficient index for all queries of different streams and the corresponding query processing mechanisms.

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9

Zhai, Hong Yu, Li Li, and Hong Hua Xu. "The Design of Query Processing in Data Stream Management System." Advanced Materials Research 952 (May 2014): 351–54. http://dx.doi.org/10.4028/www.scientific.net/amr.952.351.

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data stream management system is used to manage and query coming large, continuous, fast and flexible data stream. The system is based on the flow of data extraction, transformation, combination, which is the main content and task query execution. This paper mainly discusses the design and implementation of query execution module and query execution is composed of two parts which include query operations, query execution and scheduling.

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10

Lin, Edgar Chia Han. "Research on Multi-Attribute Sequence Query Processing Techniques over Data Streams." Applied Mechanics and Materials 513-517 (February 2014): 575–78. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.575.

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Due to the great progress of computer technology and mature development of network, more and more data are generated and distributed through the network, which is called data streams. During the last couple of years, a number of researchers have paid their attention to data stream management, which is different from the conventional database management. At present, the new type of data management system, called data stream management system (DSMS), has become one of the most popular research areas in data engineering field. Lots of research projects have made great progress in this area. Since the current DSMS does not support queries on sequence data, this paper, we will focus on multi-attribute streams, such as video films. We will discuss the related issues such as how to build an efficient index for all queries of different streams and the corresponding query processing mechanisms.

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11

Valeev, S. S., N. V. Kondratyeva, A. S. Kovtunenko, M. A. Timirov, and R. R. Karimov. "Distributed stream data processing system in multi-agent safety system of infrastructure objects." Information Technology and Nanotechnology, no. 2416 (2019): 324–31. http://dx.doi.org/10.18287/1613-0073-2019-2416-324-331.

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The solution of the problem of resource management in distributed computing systems of processing stream data in safety systems of distributed objects is considered. The tasks of streaming data processing in a multi-level multi-agent evacuation system in an infrastructure object are considered. The features of the mathematical model of a distributed stream data processing system are discussed.

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12

Ghanem, Thanaa M., Ahmed K. Elmagarmid, Per-Åke Larson, and Walid G. Aref. "Supporting views in data stream management systems." ACM Transactions on Database Systems 35, no. 1 (February 2010): 1–47. http://dx.doi.org/10.1145/1670243.1670244.

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Chandramouli, Badrish, Mohamed Ali, Jonathan Goldstein, Beysim Sezgin, and Balan Sethu Raman. "Data Stream Management Systems for Computational Finance." Computer 43, no. 12 (December 2010): 45–52. http://dx.doi.org/10.1109/mc.2010.346.

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14

Akanbi, Adeyinka, and Muthoni Masinde. "A Distributed Stream Processing Middleware Framework for Real-Time Analysis of Heterogeneous Data on Big Data Platform: Case of Environmental Monitoring." Sensors 20, no. 11 (June 3, 2020): 3166. http://dx.doi.org/10.3390/s20113166.

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In recent years, the application and wide adoption of Internet of Things (IoT)-based technologies have increased the proliferation of monitoring systems, which has consequently exponentially increased the amounts of heterogeneous data generated. Processing and analysing the massive amount of data produced is cumbersome and gradually moving from classical ‘batch’ processing—extract, transform, load (ETL) technique to real-time processing. For instance, in environmental monitoring and management domain, time-series data and historical dataset are crucial for prediction models. However, the environmental monitoring domain still utilises legacy systems, which complicates the real-time analysis of the essential data, integration with big data platforms and reliance on batch processing. Herein, as a solution, a distributed stream processing middleware framework for real-time analysis of heterogeneous environmental monitoring and management data is presented and tested on a cluster using open source technologies in a big data environment. The system ingests datasets from legacy systems and sensor data from heterogeneous automated weather systems irrespective of the data types to Apache Kafka topics using Kafka Connect APIs for processing by the Kafka streaming processing engine. The stream processing engine executes the predictive numerical models and algorithms represented in event processing (EP) languages for real-time analysis of the data streams. To prove the feasibility of the proposed framework, we implemented the system using a case study scenario of drought prediction and forecasting based on the Effective Drought Index (EDI) model. Firstly, we transform the predictive model into a form that could be executed by the streaming engine for real-time computing. Secondly, the model is applied to the ingested data streams and datasets to predict drought through persistent querying of the infinite streams to detect anomalies. As a conclusion of this study, a performance evaluation of the distributed stream processing middleware infrastructure is calculated to determine the real-time effectiveness of the framework.

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Rho, Jaeyong, Takuya Azumi, Mayo Nakagawa, Kenya Sato, and Nobuhiko Nishio. "Scheduling parallel and distributed processing for automotive data stream management system." Journal of Parallel and Distributed Computing 109 (November 2017): 286–300. http://dx.doi.org/10.1016/j.jpdc.2017.06.012.

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16

Ng, Wee Siong, Markus Kirchberg, Stephane Bressan, and Kian Lee Tan. "Towards a privacy-aware stream data management system for cloud applications." International Journal of Web and Grid Services 7, no. 3 (2011): 246. http://dx.doi.org/10.1504/ijwgs.2011.043530.

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17

Abdullah, Nibras, Putra Sumari, and Ola Ahmed Al wesabi. "Data stream management system for video on demand hybrid storage server." International Journal of Intelligent Systems Technologies and Applications 18, no. 5 (2019): 470. http://dx.doi.org/10.1504/ijista.2019.10022619.

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wesabi, Ola Ahmed Al, Putra Sumari, and Nibras Abdullah. "Data stream management system for video on demand hybrid storage server." International Journal of Intelligent Systems Technologies and Applications 18, no. 5 (2019): 470. http://dx.doi.org/10.1504/ijista.2019.101953.

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19

Wu, Ji, Kian-Lee Tan, and Yongluan Zhou. "Data-driven memory management for stream join." Information Systems 34, no. 4-5 (June 2009): 454–67. http://dx.doi.org/10.1016/j.is.2009.02.001.

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20

Kim, Taehong, Mi-Nyeong Hwang, Young-Min Kim, and Do-Heon Jeong. "Entity Resolution Approach of Data Stream Management Systems." Wireless Personal Communications 91, no. 4 (April 11, 2016): 1621–34. http://dx.doi.org/10.1007/s11277-016-3275-z.

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21

Sharaf, Mohamed A., Alexandros Labrinidis, and Panos K. Chrysanthis. "Scheduling continuous queries in data stream management systems." Proceedings of the VLDB Endowment 1, no. 2 (August 2008): 1526–27. http://dx.doi.org/10.14778/1454159.1454222.

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22

Handzlik, Adam, and Andrzej Jabłonski. "Large Data Stream Processing - Embedded Systems Design Challenges." International Journal of Electronics and Telecommunications 56, no. 2 (June 1, 2010): 107–10. http://dx.doi.org/10.2478/v10177-010-0013-4.

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Large Data Stream Processing - Embedded Systems Design Challenges The following paper describes an application of reconfigurable hardware architectures for processing of huge data streams. Radar, sonar and high speed internet networks are typical sources of data that require extreme computing power and resources to enable real time acquisition, processing and management. An approach to monitoring of real time multi-gigabit internet network has been described as a practical application of FPGA based board, designed for fast data processing.

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23

Bai, Yijian, Hetal Thakkar, Haixun Wang, and Carlo Zaniolo. "Time-Stamp Management and Query Execution in Data Stream Management Systems." IEEE Internet Computing 12, no. 6 (November 2008): 13–21. http://dx.doi.org/10.1109/mic.2008.133.

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24

CHEN, HUI. "EFFICIENTLY MINING RECENT FREQUENT PATTERNS OVER ONLINE TRANSACTIONAL DATA STREAMS." International Journal of Software Engineering and Knowledge Engineering 19, no. 05 (August 2009): 707–25. http://dx.doi.org/10.1142/s0218194009004325.

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Recent emerging applications, such as network traffic analysis, web click stream mining, power consumption measurement, sensor network data analysis, and dynamic tracing of stock fluctuation, call for study of a new kind of data, stream data. Many data stream management systems, prototype systems and software components have been developed to manage the streams or extract knowledge from stream data. Mining frequent patterns is a foundational job for the methods of data mining and knowledge discovery. This paper proposes an algorithm for mining the recent frequent patterns over an online data stream. This method uses RFP-tree to store compactly the recent frequent patterns of a stream. The content of each transaction is incrementally updated into the pattern tree upon its arrival by scanning the stream only once. Moreover, the strategy of conservative computation and time decaying model are used to ensure the correctness of the mining results. Finally, the performance results of extensive simulation show that our work can reduce the average processing time of stream data element and it is superior to other analogous algorithms.

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25

Brettlecker, Gert, and Heiko Schuldt. "Reliable distributed data stream management in mobile environments." Information Systems 36, no. 3 (May 2011): 618–43. http://dx.doi.org/10.1016/j.is.2010.10.004.

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Llaves, Alejandro, Oscar Corcho, Peter Taylor, and Kerry Taylor. "Enabling RDF Stream Processing for Sensor Data Management in the Environmental Domain." International Journal on Semantic Web and Information Systems 12, no. 4 (October 2016): 1–21. http://dx.doi.org/10.4018/ijswis.2016100101.

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This paper presents a generic approach to integrate environmental sensor data efficiently, allowing the detection of relevant situations and events in near real-time through continuous querying. Data variety is addressed with the use of the Semantic Sensor Network ontology for observation data modelling, and semantic annotations for environmental phenomena. Data velocity is handled by distributing sensor data messaging and serving observations as RDF graphs on query demand. The stream processing engine presented in the paper, morph-streams++, provides adapters for different data formats and distributed processing of streams in a cluster. An evaluation of different configurations for parallelization and semantic annotation parameters proves that the described approach reduces the average latency of message processing in some cases.

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Jajaga, Edmond, and Lule Ahmedi. "C-SWRL: A Unique Semantic Web Framework for Reasoning Over Stream Data." International Journal of Semantic Computing 11, no. 03 (September 2017): 391–409. http://dx.doi.org/10.1142/s1793351x17400165.

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The synergy of Data Stream Management Systems and Semantic Web applications has steered towards a new paradigm known as Stream Reasoning. The Semantic Web standards for knowledge base modeling and querying, namely RDF, OWL and SPARQL, has extensively been used by the Stream Reasoning community. However, the Semantic Web rule languages, such as SWRL and RIF, have never been used in stream data applications. Instead, different non-Semantic Web rule systems have been approached. Since RIF is primarily intended for exchanging rules among systems, we focused on SWRL applications with stream data. This proves difficult following the SWRL’s open world semantics. To overcome SWRL’s expressivity issues we propose an infrastructure extension, which will enable SWRL reasoning with stream data. Namely, a query processing system, such as C-SPARQL, was layered under SWRL to support closed-world and time-aware reasoning. Moreover, OWLAPI constructs were utilized to enable non-monotonicity, while SPARQL constructs were used to enable negation as failure. Water quality monitoring was used as a validation domain of the proposed system.

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Materuhin, A. V. "Problems in the development of GIS based on data stream management systems." Geodesy and Cartography 922, no. 4 (May 20, 2017): 44–47. http://dx.doi.org/10.22389/0016-7126-2017-922-4-44-47.

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The article provides the analysis of the current situation in the use of data stream management systems (DSMS) and discusses the reasons why this technology is not used to develop geographic information systems. DSMS, despite its novelty, has ceased to be a pure research project and is used in industrial applications. However, this technology is not used to design the GIS, although the necessity of processing and analyzing of spatio-temporal data streams arises in many practically important applications. The essence of the current problematic situation is the gap between new technological capabilities and the lack of a theoretical framework for the processing and analysis of spatio-temporal data streams in DSMS. Existing spatial analytics algorithms are designed for relational databases with precomputed spatial indexes and are not suitable for DSMS. The article shows that, to resolve the current problematic situation with the geoinformation systems development based on DSMS should do the following

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Wang, Chunkai, Xiaofeng Meng, Qi Guo, Zujian Weng, and Chen Yang. "Automating Characterization Deployment in Distributed Data Stream Management Systems." IEEE Transactions on Knowledge and Data Engineering 29, no. 12 (December 1, 2017): 2669–81. http://dx.doi.org/10.1109/tkde.2017.2751606.

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Elmongui, Hicham G. "Query optimization for spatio-temporal data stream management systems." SIGSPATIAL Special 1, no. 1 (March 2009): 21–26. http://dx.doi.org/10.1145/1517463.1517465.

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Poźniak, Krzysztof. "Modeling of Synchronous Data Streams Processing in the RPC Muon Trigger System of the CMS Experiment." International Journal of Electronics and Telecommunications 56, no. 4 (November 1, 2010): 489–502. http://dx.doi.org/10.2478/v10177-010-0067-3.

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Modeling of Synchronous Data Streams Processing in the RPC Muon Trigger System of the CMS ExperimentThis paper presents signal synchronization aspects in a large, distributed, multichannel RPC Muon Trigger system in the CMS experiment. The paper is an introduction to normalized structure analysis methods of such systems. The method introduces a general model of the system, presented in a form of a network of distributed, synchronous, pipeline processes. The model is based on a definition of a synchronous data stream and its formal, fundamental properties. Theoretical considerations are supported by a practical application of synchronous streams and processes management. The following processes were modeled and implemented in hardware: window synchronization, derandomization, data concentration and generation of test pulses. There are presented chosen results of the model application in the CMS experiment.

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Xiao, Guoqing, Kenli Li, Xu Zhou, and Keqin Li. "Queueing Analysis of Continuous Queries for Uncertain Data Streams Over Sliding Windows." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 09 (November 2016): 1660001. http://dx.doi.org/10.1142/s0218001416600016.

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With the rapid development of data collection methods and their practical applications, the management of uncertain data streams has drawn wide attention in both academia and industry. System capacity planning and Quality of service (QoS) metrics are two very important problems for data stream management systems (DSMSs) to process streams efficiently due to unpredictable input characteristics and limited memory resource in the system. Motivated by this, in this paper, we explore an effective approach to estimate the memory requirement, data loss ratio, and tuple latency of continuous queries for uncertain data streams over sliding windows in a DSMS. More specifically, we propose a queueing model to address these problems in this paper. We study the average number of tuples, average tuple latency in the queue, and the distribution of the number of tuples and tuple latency in the queue under the Poisson arrival of input data streams in our queueing model. Furthermore, we also determine the maximum capacity of the queueing system based on the data loss ratio. The solutions for the above problems are very important to help researchers design, manage, and optimize a DSMS, including allocating buffer needed for a queue and admitting a continuous uncertain query to the system without violation of the pre-specified QoS requirements.

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Breve, Bernardo, Loredana Caruccio, Stefano Cirillo, Vincenzo Deufemia, and Giuseppe Polese. "Dependency Visualization in Data Stream Profiling." Big Data Research 25 (July 2021): 100240. http://dx.doi.org/10.1016/j.bdr.2021.100240.

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Cai, Walter, Philip A. Bernstein, Wentao Wu, and Badrish Chandramouli. "Optimization of threshold functions over streams." Proceedings of the VLDB Endowment 14, no. 6 (February 2021): 878–89. http://dx.doi.org/10.14778/3447689.3447693.

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A common stream processing application is alerting, where the data stream management system (DSMS) continuously evaluates a threshold function over incoming streams. If the threshold is crossed, the DSMS raises an alarm. The threshold function is often calculated over two or more streams, such as combining temperature and humidity readings to determine if moisture will form on a machine and therefore cause it to malfunction. This requires taking a temporal join across the input streams. We show that for the broad class of functions called quasiconvex functions, the DSMS needs to retain very few tuples per-data-stream for any given time interval and still never miss an alarm. This surprising result yields a large memory savings during normal operation. That savings is also important if one stream fails, since the DSMS would otherwise have to cache all tuples in other streams until the failed stream recovers. We prove our algorithm is optimal and provide experimental evidence that validates its substantial memory savings.

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TIAN, Hai-sheng. "Calculating Max and Min with exemplary sketch algorithm in data stream management system." Journal of Computer Applications 28, no. 8 (August 20, 2008): 1986–90. http://dx.doi.org/10.3724/sp.j.1087.2008.01986.

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Yamaguchi, Akihiro, Yousuke Watanabe, Kenya Sato, Yukikazu Nakamoto, Yoshiharu Ishikawa, Shinya Honda, and Hiroaki Takada. "In-vehicle Distributed Time-critical Data Stream Management System for Advanced Driver Assistance." Journal of Information Processing 25 (2017): 107–20. http://dx.doi.org/10.2197/ipsjjip.25.107.

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Maison, Rafal, Ewelina Majda, Andrzej P. Dobrowolski, and Maciej Zakrzewicz. "Similarity based join over audio feeds in a multimedia data stream management system." Bell Labs Technical Journal 18, no. 1 (June 2013): 195–212. http://dx.doi.org/10.1002/bltj.21599.

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Guertault, Lucie, Garey Fox, and Shannon Brewer. "Geomorphic identification of physical habitat features in a large, altered river system." E3S Web of Conferences 40 (2018): 02031. http://dx.doi.org/10.1051/e3sconf/20184002031.

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Altered flow regimes in streams can significantly affect ecosystems and disturb ecological processes, leading to species loss and extinction. Many river management projects use stream classification and habitat assessment approaches to design practical solutions to reverse or mitigate adverse effects of flow regime alteration on stream systems. The objective of this study was to develop a methodology to provide a primary identification of physical habitats in an 80-km long segment of the Canadian River in central Oklahoma. The methodology relied on basic geomorphic variables describing the stream and its floodplain that were derived from aerial imagery and Lidar data using Geographic Information Systems. Geostatistical tests were implemented to delineate habitat units. This approach based on high resolution data and did not require in-site inspection provided a relatively refined habitat delineation, consistent with visual observations. Future efforts will focus on validation via field surveys and coupling with hydro-sedimentary modeling to provide a tool for environmental flow decisions.

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Han, Jiawei, Yixin Chen, Guozhu Dong, Jian Pei, Benjamin W. Wah, Jianyong Wang, and Y. Dora Cai. "Stream Cube: An Architecture for Multi-Dimensional Analysis of Data Streams." Distributed and Parallel Databases 18, no. 2 (September 2005): 173–97. http://dx.doi.org/10.1007/s10619-005-3296-1.

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Vasconcelos, Rafael Oliveira, Markus Endler, Berto de Tácio Pereira Gomes, and Francisco José da Silva e Silva. "Design and Evaluation of an Autonomous Load Balancing System for Mobile Data Stream Processing Based On a Data Centric Publish Subscribe Approach." International Journal of Adaptive, Resilient and Autonomic Systems 5, no. 3 (July 2014): 1–19. http://dx.doi.org/10.4018/ijaras.2014070101.

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Several new applications of mobile computing environments, such as Intelligent Transportation Systems, Fleet Management and Logistics, and integrated Industrial Process Automation share the requirement of remote monitoring and high performance processing of huge data streams produced by large sets of mobile nodes. Two key requirements for the deployment and operation of such mobile infrastructures are the handling of large and variable numbers of wireless connections to the monitored mobile nodes regardless of their current use or locations, and to automatically adapt to variations in the volume of the mobile data streams. This article describes the design, implementation, and evaluation of an autonomic mechanism for load balancing of mobile data streams. The autonomic capability has been incorporated into a scalable middleware system based on a Data Centric Publish Subscribe approach using the OMG Data Distribution Service (DDS) standard and aimed at real-time and adaptive handling of mobile connectivity and data stream processing for great sets of mobile nodes. A significant amount of evaluation experiments of the proposed infrastructure is presented, reinforcing its viability and the benefits arising from the use of an autonomic approach to handle the requirements of high variability and scalability.

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Halstead, Ben, Yun Sing Koh, Patricia Riddle, Russel Pears, Mykola Pechenizkiy, and Albert Bifet. "Recurring concept memory management in data streams: exploiting data stream concept evolution to improve performance and transparency." Data Mining and Knowledge Discovery 35, no. 3 (February 14, 2021): 796–836. http://dx.doi.org/10.1007/s10618-021-00736-w.

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Kamaludin, Hazalila, Hairulnizam Mahdin, and Jemal H. Abawajy. "Filtering Redundant Data from RFID Data Streams." Journal of Sensors 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/7107914.

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Radio Frequency Identification (RFID) enabled systems are evolving in many applications that need to know the physical location of objects such as supply chain management. Naturally, RFID systems create large volumes of duplicate data. As the duplicate data wastes communication, processing, and storage resources as well as delaying decision-making, filtering duplicate data from RFID data stream is an important and challenging problem. Existing Bloom Filter-based approaches for filtering duplicate RFID data streams are complex and slow as they use multiple hash functions. In this paper, we propose an approach for filtering duplicate data from RFID data streams. The proposed approach is based on modified Bloom Filter and uses only a single hash function. We performed extensive empirical study of the proposed approach and compared it against the Bloom Filter, d-Left Time Bloom Filter, and the Count Bloom Filter approaches. The results show that the proposed approach outperforms the baseline approaches in terms of false positive rate, execution time, and true positive rate.

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Endler, Markus, Jean-Pierre Briot, Vitor P. de Almeida, Ruhan dos Reis, and Francisco Silva e Silva. "Stream-Based Reasoning for IoT Applications — Proposal of Architecture and Analysis of Challenges." International Journal of Semantic Computing 11, no. 03 (September 2017): 325–44. http://dx.doi.org/10.1142/s1793351x1740013x.

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As distributed IoT applications become larger and more complex, the pure processing of raw sensor and actuation data streams becomes impractical. Instead, data streams must be fused into tangible facts and these pieces of information must be combined with a background knowledge to infer new pieces of knowledge. And since many IoT applications require almost real-time reactivity to stimulus of the environment, such information inference process has to be performed in a continuous, on-line manner. This paper proposes a new semantic model for data stream processing and real-time reasoning based on the concepts of Semantic Stream and Fact Stream, as a natural extension of Complex Event Processing (CEP) and RDF (graph-based knowledge model). The main advantages of our approach are that: (a) it considers time as a key relation between pieces of information; (b) the processing of streams can be implemented using CEP; (c) it is general enough to be applied to any Data Stream Management System (DSMS). We describe a scenario about patients flux monitoring in a hospital as an example of prospective application. Last, we present challenges and prospects on using machine learning and induction algorithms to learn abstractions and reasoning rules from a continuous data stream.

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Vasconcelos, Márlon de Castro, Adriano Sanches Melo, and Albano Schwarzbold. "Comparing the performance of different stream classification systems using aquatic macroinvertebrates." Acta Limnologica Brasiliensia 25, no. 4 (December 2013): 406–17. http://dx.doi.org/10.1590/s2179-975x2013000400006.

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AIM: We evaluated five stream classification systems observing: 1) differences in richness, abundance and macroinvertebrates communities among stream classes within classification systems; and 2) whether classification systems present better performance using macroinvertebrates. Additionally, we evaluated the effects of taxonomic resolution and data type (abundance and presence) on results. METHODS: Five stream classification systems were used, two based on hydroregions, one based on ecoregions by FEOW, a fourth one based on stream orders and the last one based on clusters of environment variables sampled in 37 streams at Rio Grande do Sul state, Brazil. We used a randomization test to evaluate differences of richness and abundance, a db-MANOVA to evaluate the differences of species assemblages and Classification Strength (CS) to evaluate the classifications performance. RESULTS: There were differences of richness and abundance among stream classes within each stream classification. The same result was found for community data, except for stream order classifications in family level. We observed that stream classes obtained for each stream classification differed in terms of environment variables (db-MANOVA). The classification based on environment variables showed higher CS values than other classification systems. The taxonomic resolution was important to the observed results. Data on genera level presented CS values 12% higher than family level for cluster classification, and the data type was dependent on the classification system and taxonomic resolution employed. CONCLUSION: Our results indicate that classifications based on cluster of environment variables was better than other stream classification systems, and similar results using genera level can be obtained for management programs using family resolution in a geographical context similar to this study.

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Skatkov, A. V., and LA Balakireva. "Guaranteed evaluation of operating characteristics of quality environmental monitoring systems." Monitoring systems of environment, no. 1 (March 22, 2017): 66–74. http://dx.doi.org/10.33075/2220-5861-2017-1-66-74.

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This paper proposes an approach for solving management model environment objects parametric adaptation based on the queuing systems domination procedures with random input data stream problem. A constructing majorizing system algorithm based on the majorization problem solution is proposed. The system provides environment monitoring system structure with its performance, input data stream intensity and processing labor-intensive balancing. The detailed experiment findings are discussed.

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Altier, Lee S., R. Richard Lowrance, and R. G. Williams. "078 REDUCING AGRICULTURAL POLLUTION WITH RIPARIAN BUFFER SYSTEMS." HortScience 29, no. 5 (May 1994): 439c—439. http://dx.doi.org/10.21273/hortsci.29.5.439c.

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Even with careful management, within-field practices are often insufficient to prevent considerable nonpoint source pollution to adjacent streams. Water resources suffer from sediment, N, and P transported in surface runoff and N in subsurface movement when fields are cultivated up to stream banks. The maintainance of forested buffer systems between farmland and streams has been proposed as a remedy for mitigating pollution. Chemical movement through such a buffer system has been monitored for several years at the University of Georgia Coastal Plain Experiment Station. With the aid of that data, the Riparian Ecosystem Management Model is being developed to simulate biological, chemical, and hydrologic processes in order to evaluate the effectiveness of buffer system management for reducing the influx of pollutants to streams. The model allows an examination of the long-term potential of a buffer system under changing environmental conditions.

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Tzagkarakis, George, Aleka Seliniotaki, Vassilis Christophides, and Panagiotis Tsakalides. "Uncertainty-Aware Sensor Data Management and Early Warning for Monitoring Industrial Infrastructures." International Journal of Monitoring and Surveillance Technologies Research 2, no. 4 (October 2014): 1–24. http://dx.doi.org/10.4018/ijmstr.2014100101.

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In several industrial applications, monitoring large-scale infrastructures in order to provide notifications for abnormal behavior is of high significance. For this purpose, the deployment of large-scale sensor networks is the current trend. However, this results in handling vast amounts of low-level, and often unreliable, data, while an efficient and real-time data manipulation is a strong demand. In this paper, the authors propose an uncertainty-aware data management system capable of monitoring interrelations between large and heterogeneous sensor data streams in real-time. To this end, an efficient similarity function is employed instead of the typical correlation coefficient to monitor dynamic phenomena for timely alerting notifications, and to guarantee the validity of detected extreme events. Experimental evaluation with a set of real data recorded by distinct sensors in an industrial water desalination plant reveals a superior performance of our proposed approach in terms of achieving significantly reduced execution times, along with increased accuracy in detecting extreme events and highly correlated pairs of sensor data streams, when compared with state-of-the-art data stream processing techniques.

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Abadi, Daniel J., Don Carney, Ugur �etintemel, Mitch Cherniack, Christian Convey, Sangdon Lee, Michael Stonebraker, Nesime Tatbul, and Stan Zdonik. "Aurora: a new model and architecture for data stream management." VLDB Journal The International Journal on Very Large Data Bases 12, no. 2 (August 1, 2003): 120–39. http://dx.doi.org/10.1007/s00778-003-0095-z.

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Pradeepa, D., R. Valarmady, and S. G Rajasekar. "Elegant System for Library Management using RFID." International Journal of Engineering & Technology 7, no. 3.1 (August 4, 2018): 133. http://dx.doi.org/10.14419/ijet.v7i3.1.16816.

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Radio Frequency Identification (RFID) implies a system that exchanges the data remotely, utilizing radio frequency waves. It is programmed identification innovation. This paper is about RFID based system for library management that permits quick exchange stream and will make simple to deal with the exercises like issue and return of books from the library absent much manual intercession. This system depends on RFID readers and detached RFID tags that can store the data electronically which can be perused by the RFID readers. This system will influence clients to issue to and return of books through RFID tags simple and furthermore ascertain the comparing fine connected with the timeframe the nonappearance of the book from the library.

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Li, Haifeng, and Hong Chen. "Mining non-derivable frequent itemsets over data stream." Data & Knowledge Engineering 68, no. 5 (May 2009): 481–98. http://dx.doi.org/10.1016/j.datak.2009.01.002.

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