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4th International Symposium on Computing and Networking (CANDAR)

作者： Xu, Jiwang Nakamoto, Yukikazu Akiyama, Shingo Univ Hyogo Grad Sch Appl Informat Kobe Hyogo 6500044 Japan

ISBN: (纸本)9781509026555

This paper presents a mobile workflow support system based on mobile stream data management system. A stream data management system is a management system for data generated continually and is used for financial applications, network traffic systems, sensor networks. We developed a mobile data stream management system for Android devices (mDSMS) and applied vehicle data services and information sharing in collaborative works. DSMS not only matches execution of workflow but also facilitates the workflow descriptions to respond to external events in an organization. We present implementation of workflow support using mDSMS and its evaluation based on our development experience, comparison with a workflow pattern, and its performance.

关键词： Workflow support data stream management system Android

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Workflow Support Based on Mobile data stream management system

Workflow Support Based on Mobile Data Stream Management Syst...

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International Symposium on Computing and Networking (CANDAR)

作者： Jiwang Xu Yukikazu Nakamoto Shingo Akiyama Graduate School of Applied Informatics University of Hyogo Japan

ISBN: (纸本)9781509026562

关键词： Android Workflow support data stream management system

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A Proposal for Mobile Collaborative Work Support Platform Using an Embedded data stream management system 35

A Proposal for Mobile Collaborative Work Support Platform Us...

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IEEE 35th International Conference on Distributed Computing systems Workshops ICDCSW 2015

作者： Nakamoto, Yukikazu Akiyama, Shingo Univ Hyogo Grad Sch Appl Informat Kobe Hyogo 6500044 Japan

ISBN: (纸本)9781467373036

This paper presents a mobile collaborative work support platform that uses an embedded data stream management system (mDSMS). The collaborative work that we focus on includes activities not only within typical organizations, but also in organizations such as disaster relief centers. These activities are modeled using a meeting-oriented coordination model of distributed systems, because such activities are temporally coupled and referentially uncoupled. We develop a collaborative work support system for Android devices that uses data stream management, which is a key technique of the model. One of the features in mDSMS is a multilayer query scheduling feature that is capable of various holding times for activity messages and assures the timeliness of message processing. We present the architecture and design of mDSMS on the Android platform as well as preliminary evaluation results.

关键词： Mobile collaborative work support system data stream management system Android

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AEDSMS: Automotive Embedded data stream management system 31

AEDSMS: Automotive Embedded Data Stream Management System

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31st IEEE International Conference on data Engineering

作者： Yamaguchi, Akihiro Nakamoto, Yukikazu Sato, Kenya Ishikawa, Yoshiharu Watanabe, Yousuke Honda, Shinya Takada, Hiroaki Nagoya Univ Ctr Embedded Comp Syst Nagoya Aichi Japan Univ Hyogo Grad Sch Appl Informat Kobe Hyogo Japan Doshisha Univ Mobil Res Ctr Kyoto Japan Nagoya Univ Grad Sch Informat Sci Nagoya Aichi Japan Nagoya Univ Inst Innovat Future Soc Nagoya Aichi Japan

ISBN: (纸本)9781479979646

data stream management systems (DSMSs) are useful for the management and processing of continuous data at a high input rate with low latency. In the automotive domain, embedded systems use a variety of sensor data and communications from outside the vehicle to promote autonomous and safe driving. Thus, the software developed for these systems must be capable of handling large volumes of data and complex processing. At present, we are developing a platform for the integration and management of data in an automotive embedded system using a DSMS. However, compared with conventional DSMS fields, we have encountered new challenges such as precompiling queries when designing automotive systems (which demands time predictability), distributed stream processing in in-vehicle networks, and real-time scheduling and sensor data fusion by stream processing. Therefore, we developed an automotive embedded DSMS (AEDSMS) to address these challenges. The main contributions of the present study are: (1) a clear understanding of the challenges faced when introducing DSMSs into the automotive field;(2) the development of AEDSMS to tackle these challenges;and (3) an evaluation of AEDSMS during runtime using a driving assistance application.

关键词： data stream management system automotive engineering Sensor data fusion time schedule large volume data Sensor data Embedded systems streams Runtime Vehicle Network Communication Research

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CONTENT-BASED LOAD SHEDDING IN MULTIMEDIA data stream management system

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FOUNDATIONS OF COMPUTING AND DECISION SCIENCES 2012年第2期37卷 79-95页

作者： Maison, Rafal Zakrzewicz, Maciej Poznan Univ Tech Inst Comp Sci Ul Piotrowo 2 PL-60965 Poznan Poland

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.

关键词： data streams load shedding data stream management system overload management

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SMM: a data stream management system for Knowledge Discovery

SMM: a Data Stream Management System for Knowledge Discovery

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IEEE 27th International Conference on data Engineering (ICDE 2011)

作者： Thakkar, Hetal Laptev, Nikolay Mousavi, Hamid Mozafari, Barzan Russo, Vincenzo Zaniolo, Carlo Google Inc. USA

ISBN: (纸本)9781424489589

The problem of supporting data mining applications proved to be difficult for database management systems and it is now proving to be very challenging for data stream management systems (DSMSs), where the limitations of SQL are made even more severe by the requirements of continuous queries. The major technical advances that achieved separately on DSMSs and on data stream mining algorithms have failed to converge and produce powerful data stream mining systems. Such systems, however, are essential since the traditional pull-based approach of cache mining is no longer applicable, and the push-based computing mode of data streams and their bursty traffic complicate application development. For instance, to write mining applications with quality of service (QoS) levels approaching those of DSMSs, a mining analyst would have to contend with many arduous tasks, such as support for data buffering, complex storage and retrieval methods, scheduling, fault-tolerance, synopsis-management, load shedding, and query optimization. Our stream Mill Miner (SMM) system solves these problems by providing a data stream mining workbench that combines the ease of specifying high-level mining tasks, as in Weka, with the performance and QoS guarantees of a DSMS. This is accomplished in three main steps. The first is an open and extensible DSMS architecture where KDD queries can be easily expressed as user-defined aggregates (UDAs)-our system combines that with the efficiency of synoptic data structures and mining-aware load shedding and optimizations. The second key component of SMM is its integrated library of fast mining algorithms that are light enough to be effective on data streams. The third advanced feature of SMM is a Mining Model Definition Language (MMDL) that allows users to define the flow of mining tasks, integrated with a simple box&arrow GUI, to shield the mining analyst from the complexities of lower-level queries. SMM is the first DSMS capable of online mining and this pap

关键词： Aggregates DSMS architecture data mining Graphical user interfaces Humidity KDD query Libraries Quality of service SMM SQL SQL Training cache mining data buffering data mining data mining application data stream management system database management system database management systems fault tolerance integrated library knowledge discovery mining aware load shedding mining model definition language pull based approach push based computing mode quality of service level query languages query optimization query processing retrieval method stream mill miner system synopsis management synoptic data structure user defined aggregate

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A Comprehensive Survey on Parallelization and Elasticity in stream Processing

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ACM COMPUTING SURVEYS 2019年第2期52卷 36-36页

作者： Roeger, Henriette Mayer, Ruben Univ Stuttgart Inst Parallel & Distributed Syst Stuttgart Germany Tech Univ Munich Informat 13 Chair Applicat & Middleware Syst Boltzmannstr 3 D-85748 Garching Germany IPVS Univ Str 38 D-70569 Stuttgart Germany

stream Processing (SP) has evolved as the leading paradigm to process and gain value from the high volume of streaming data produced, e.g., in the domain of the Internet of Things. An SP system is a middleware that deploys a network of operators between data sources, such as sensors, and the consuming applications. SP systems typically face intense and highly dynamic data streams. Parallelization and elasticity enable SP systems to process these streams with continuous high quality of service. The current research landscape provides a broad spectrum of methods for parallelization and elasticity in SP. Each method makes specific assumptions and focuses on particular aspects. However, the literature lacks a comprehensive overview and categorization of the state of the art in SP parallelization and elasticity, which is necessary to consolidate the state of the research and to plan future research directions on this basis. Therefore, in this survey, we study the literature and develop a classification of current methods for both parallelization and elasticity in SP systems.

关键词： stream processing complex event processing data stream management system parallelization elasticity

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Distributed streaming data Processing in IoT systems Using Multi-agent Software Architecture 18th

Distributed Streaming Data Processing in IoT Systems Using M...

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18th International Conference on Next Generation Teletraffic and Wired/Wireless Advanced Networks and systems (NEW2AN) / 11th Conference on Internet of Things and Smart Spaces (ruSMART)

作者： Kovtunenko, Alexey Bilyalov, Azat Valeev, Sagit Ufa State Aviat Tech Univ Karl Marx St 12 Ufa Russia Bashkir State Med Univ Teatralnaya St 2a Ufa Russia

ISBN: (纸本)9783030011680;9783030011673

The problem of distributed storing and processing of streaming data in IoT systems is considered. A mathematical model and agent-based software architecture for distributed streaming data processing over heterogeneous computer network is offered. The software architecture determines the following features of IoT nodes software: structure of software components, models of interoperability, algorithms of resource management and also xml-based language which allows to descript distributed IoT applications. The offered architecture is implemented as a software framework ABSynth.

关键词： Internet of Things streaming data Software architecture Distributed control system data stream management system

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Entity Resolution Approach of data stream management systems

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WIRELESS PERSONAL COMMUNICATIONS 2016年第4期91卷 1621-1634页

作者： Kim, Taehong Hwang, Mi-Nyeong Kim, Young-Min Jeong, Do-Heon KISTI Disaster Management HPC Technol Res Ctr 245 Daehak Ro Daejeon South Korea

Owing to the technological advancements in Semantic Web and sensor networks, a large amount of data has been produced in association with the open data policy. However, data stream management systems that process stream data have focused on the processing of a large amount of data with little priority on data identification, integration, and external linkage. Furthermore, entity resolution is focused mainly on static database-based technologies. In this study, a real-time stream data processing architecture that can perform the integration and entity resolution of streaming-type heterogeneous input data and interlink with external data is designed. To achieve this goal, a light adapter to integrate heterogeneous data into standard scheme and blocking technique to reduce comparison candidates are applied. The implemented data adapters shows 4 times higher throughput than open source data parsers and the entity resolution results with streaming data shows similar performance with the static data sets. The proposed streaming data entity resolution architecture is expected to form the basis of data integration research that can integrate various information sources of data efficiently, enrich internal data.

关键词： IoT streaming data processing Dynamic entity resolution streaming linked data Entity resolution Blocking entity data stream management system

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Avoiding class warfare: managing continuous queries with differentiated classes of service

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VLDB JOURNAL 2016年第2期25卷 197-221页

作者： Pham, Thao N. Chrysanthis, Panos K. Labrinidis, Alexandros Univ Pittsburgh Dept Comp Sci Pittsburgh PA 15260 USA

data stream management systems (DSMSs) offer the most effective solution for processing data streams by efficiently executing continuous queries (CQs) over the incoming data. CQs inherently have different levels of criticality and hence different levels of expected quality of service (QoS) and quality of data (QoD). Adhering to such expected QoS/QoD metrics is even more important in cases of multi-tenant data stream management services. In this work, we propose DILoS, a framework that, through priority-based scheduling and load shedding, supports differentiated QoS and QoD for multiple classes of CQs. Unlike existing works that consider scheduling and load shedding separately, DILoS is a novel unified framework that exploits the synergy between scheduling and load shedding. We also propose ALoMa, a general, adaptive load manager that DILoS is built upon. By its design, ALoMa performs better than the state-of-the-art alternatives in three dimensions: (1) it automatically tunes the headroom factor, (2) it honors the delay target, (3) it is applicable to complex query networks with shared operators. We implemented DILoS and ALoMa in our real DSMS prototype system (AQSIOS) and evaluate their performance for a variety of real and synthetic workloads. Our experimental evaluation of ALoMa verified its clear superiority over the state-of-the-art approaches. Our experimental evaluation of the DILoS framework showed that it (a) allows the scheduler and load shedder to consistently honor CQs' priorities, (b) significantly increases system capacity utilization by exploiting batch processing, and (c) enables operator sharing among query classes of different priorities while avoiding priority inversion, i.e., a lower-priority class never blocks a higher-priority one.

关键词： data stream management system Continuous query Multi-tenant Load shedding Scheduling

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