Data mining and knowledge discovery technologies

"This book presents researchers and practitioners in fields such as knowledge management, information science, Web engineering, and medical informatics, with comprehensive, innovative research on data mining methods, structures, tools, and methods, the knowledge discovery process, and data marts, among many other cutting-edge topics"--Provided by publisher

Messaging in distributed systems

Different middleware technologies have been facilitating the communication between the distributed applications. RMI and CORBA are the most commonly used technology for communication between distributed components. This research work was undertaken in view of increasing e-business requirements. The Transfer of Messages in Distributed Systems (TMDS architecture) proposed in this paper is based on the concept of asynchronous communication between components. The advantage of the proposed architecture is that the sender of the message can continue processing after sending the message and need not wait for the reply from other application. TMDS Architecture ensures guaranteed delivery of the message. TMDS Architecture supports the two major domain of asynchronous messaging: (i) Publish/Subscribe and (ii) Point-To-Point domains. In addition TMDS architecture has the facility to prioritize the message, filter messages on certain conditions and the architecture also supports easy integration of new systems with the existing legacy systems

Distributed scheduler for high performance data-centric systems

Amount of data stored in enterprises are increasing rapidly. Volume of data stored in database is approaching to terabyte size. Response time is directly proportional to the amount of data in databases. Requirement of fast response time under these circumstances have motivated the research of parallel database systems (PDS) during last decade. Despite distribution of data in PDS to various processing elements (PE), concurrency control algorithms uses centralized scheduling approach. This approach has inherent weakness, under heavy load conditions, such as - big lock table, more number of messages in the system, central overloaded scheduler. In this paper we distribute the scheduling responsibilities to the nodes where data is actually located. We also propose a new serializability criterion, parallel database quasi-serializability, to meet these requirements

Failure recovery in grid database systems

Failure is unavoidable in any computing environment and hence any computing architecture must address recovery issues. Recovery becomes more complicated when sites are distributed, autonomous and heterogeneous. Grid architecture is such an evolving distributed architecture. Databases operating in Grid architecture have different recovery issues than their other distributed counterparts - distributed and multidatabase. In this paper we focus on maintaining correctness of data in case of site failure in Grid database

Semantic XPath query transformation: Opportunities and performance

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)4443 LNCS994-100

The Digital Emerging and Converging Bits of Urbanism: Crowddesigning a live knowledge network for sustainable urban living

Data is ubiquitous in our cities. However, designing a knowledge  network about our cities is an arduous task, given that data sensed cannot be used directly, human significance must be added. Adding human significance can be achieved via an automated “expert system (ES)” in which domain expert knowledge are stored in a knowledge-based repository. The domain expert knowledge is matched with the corresponding data to derive specific inference which can aid decision making for urban stakeholders. This requires amalgamation of various interdisciplinary techniques. This paper presents a survey of existing technologies in order to investigate the emerging issues surrounding the design of a live knowledge network for sustainable urban living. The maps and models of the existing infrastructure of our cities that include a wealth of information such as topography, layout, zoning, land use, transportation networks, public facilities, and resource network grids need to be integrated with real-time spatiotemporal information about the city. Public data in forms of archives and data streams as well as online data from the social network and the Web can be analyzed using data mining techniques. The domain experts need to interpret the results of data mining into knowledge that will augment the existing knowledge base and models of our cities. In addition to the analysis of archived and streamed data sources from the built environment, the emerging state-of-the-art Web 2.0 and mobile technologies are presented as the potential techniques to crowddesign a live urban knowledge network. Data modelling, data mining, crowdsourcing, and social intervention techniques are reviewed in this paper with examples from the related work and our own experiments

Using semantics for XPath query transformation

10.1504/IJWGS.2010.032191International Journal of Web and Grid Services6158-9