Data Vitalization's Perspective Towards Smart City: A Reference Model for Data Service Oriented Architecture

The data are complexity and heterogeneous in the city-scale. When the tasks are nonlinear, existing systems cannot perform well. This paper proposes a data service oriented architecture that is based on the data vitalization theory. In this perspective, the vitalized cells are the basic units of a system, which are organized through nested and/or layer structure. A smart service platform bases on crowd intelligence network is developed according the theory of data vitalization. It allows the developers upload their own services and data to open for other developers. Then the architecture and implement technologies of the platform are introduced. At last, an application example of this architecture is given, which is used to sense social hot-spots in a period and rebuild the scene of the hot-spots. The platform and example show that the data vitalization theory achieves high scalability and flexibility for both linear and non-linear tasks.

[1]  Qinghua Zheng,et al.  A Novel Approach to Improving the Efficiency of Storing and Accessing Small Files on Hadoop: A Case Study by PowerPoint Files , 2010, 2010 IEEE International Conference on Services Computing.

[2]  Himabindu Pucha,et al.  Towards Optimizing Hadoop Provisioning in the Cloud , 2009, HotCloud.

[3]  Martin Bichler,et al.  Service-oriented computing , 2006, Computer.

[4]  Lei Chen,et al.  Data Vitalization: A New Paradigm for Large-Scale Dataset Analysis , 2010, 2010 IEEE 16th International Conference on Parallel and Distributed Systems.

[5]  M. Brian Blake,et al.  Service-Oriented Computing and Cloud Computing: Challenges and Opportunities , 2010, IEEE Internet Computing.

[6]  Ralf Lämmel,et al.  Google's MapReduce programming model - Revisited , 2007, Sci. Comput. Program..

[7]  Vitaly Shmatikov,et al.  Airavat: Security and Privacy for MapReduce , 2010, NSDI.

[8]  Jeffrey Dean,et al.  Keynote talk: Experiences with MapReduce, an abstraction for large-scale computation , 2006, 2006 International Conference on Parallel Architectures and Compilation Techniques (PACT).

[9]  Douglas Stott Parker,et al.  Map-reduce-merge: simplified relational data processing on large clusters , 2007, SIGMOD '07.

[10]  Geoffrey C. Fox,et al.  MapReduce for Data Intensive Scientific Analyses , 2008, 2008 IEEE Fourth International Conference on eScience.

[11]  Frank van Harmelen,et al.  Scalable Distributed Reasoning Using MapReduce , 2009, SEMWEB.

[12]  Hairong Kuang,et al.  The Hadoop Distributed File System , 2010, 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST).

[13]  Raouf Boutaba,et al.  Cloud computing: state-of-the-art and research challenges , 2010, Journal of Internet Services and Applications.

[14]  Abraham Silberschatz,et al.  HadoopDB: An Architectural Hybrid of MapReduce and DBMS Technologies for Analytical Workloads , 2009, Proc. VLDB Endow..

[15]  Bhavani M. Thuraisingham,et al.  Storage and Retrieval of Large RDF Graph Using Hadoop and MapReduce , 2009, CloudCom.

[16]  H. Farhangi,et al.  The path of the smart grid , 2010, IEEE Power and Energy Magazine.

[17]  Anastasios Kementsietsidis,et al.  Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data , 2001, SIGMOD 2011.

[18]  Komal Shringare,et al.  Apache Hadoop Goes Realtime at Facebook , 2015 .

[19]  Sanjay Ghemawat,et al.  MapReduce: a flexible data processing tool , 2010, CACM.

[20]  Mike P. Papazoglou,et al.  Service-oriented computing: concepts, characteristics and directions , 2003, Proceedings of the Fourth International Conference on Web Information Systems Engineering, 2003. WISE 2003..

[21]  Michael Stonebraker,et al.  MapReduce and parallel DBMSs: friends or foes? , 2010, CACM.

[22]  Xubin He,et al.  Implementing WebGIS on Hadoop: A case study of improving small file I/O performance on HDFS , 2009, 2009 IEEE International Conference on Cluster Computing and Workshops.

[23]  Michael C. Schatz,et al.  CloudBurst: highly sensitive read mapping with MapReduce , 2009, Bioinform..

[24]  Michele Zorzi,et al.  The Deployment of a Smart Monitoring System Using Wireless Sensor and Actuator Networks , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[25]  A. Y. Chang,et al.  Development of Practical Smart House Scenario Control System , 2013 .

[26]  Zhaohui Wu,et al.  Trace analysis and mining for smart cities: issues, methods, and applications , 2013, IEEE Communications Magazine.

[27]  Yun Tian,et al.  Improving MapReduce performance through data placement in heterogeneous Hadoop clusters , 2010, 2010 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW).

[28]  Christoforos E. Kozyrakis,et al.  On the energy (in)efficiency of Hadoop clusters , 2010, OPSR.

[29]  Thomas Hofmann,et al.  Map-Reduce for Machine Learning on Multicore , 2007 .

[30]  Randy H. Katz,et al.  Improving MapReduce Performance in Heterogeneous Environments , 2008, OSDI.

[31]  Luis Rodero-Merino,et al.  A break in the clouds: towards a cloud definition , 2008, CCRV.

[32]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[33]  Wei-Tek Tsai,et al.  Service-Oriented Cloud Computing Architecture , 2010, 2010 Seventh International Conference on Information Technology: New Generations.

[34]  Christoforos E. Kozyrakis,et al.  Evaluating MapReduce for Multi-core and Multiprocessor Systems , 2007, 2007 IEEE 13th International Symposium on High Performance Computer Architecture.

[35]  Klaus Schmid,et al.  Variability in Service-Oriented Systems: An Analysis of Existing Approaches , 2012, ICSOC.

[36]  Zheng Shao,et al.  Hive - a petabyte scale data warehouse using Hadoop , 2010, 2010 IEEE 26th International Conference on Data Engineering (ICDE 2010).

[37]  Beng Chin Ooi,et al.  Proceedings of the 2007 ACM SIGMOD international conference on Management of data , 2007, SIGMOD 2007.

[38]  Paolo Traverso,et al.  Service-Oriented Computing: a Research Roadmap , 2008, Int. J. Cooperative Inf. Syst..

[39]  Andrea Vitaletti,et al.  Smart City: An Event Driven Architecture for Monitoring Public Spaces with Heterogeneous Sensors , 2010, 2010 Fourth International Conference on Sensor Technologies and Applications.

[40]  Naga K. Govindaraju,et al.  Mars: A MapReduce Framework on graphics processors , 2008, 2008 International Conference on Parallel Architectures and Compilation Techniques (PACT).

[41]  Alekh Jindal,et al.  Hadoop++ , 2010 .

[42]  Alan L. Cox,et al.  The Hadoop distributed filesystem: Balancing portability and performance , 2010, 2010 IEEE International Symposium on Performance Analysis of Systems & Software (ISPASS).

[43]  Thomas Sandholm,et al.  Dynamic Proportional Share Scheduling in Hadoop , 2010, JSSPP.

[44]  A. R. Mahmud,et al.  SOA of Smart City Geospatial Management , 2009, 2009 Third UKSim European Symposium on Computer Modeling and Simulation.

[45]  Ronald C. Taylor An overview of the Hadoop/MapReduce/HBase framework and its current applications in bioinformatics , 2010, BMC Bioinformatics.

[46]  Ioannis Chatzigiannakis,et al.  Developing an IoT Smart City framework , 2013, IISA 2013.