Transactions on Large-Scale Data- and Knowledge-Centered Systems VIII

The LNCS journal Transactions on Large-Scale Data- and Knowledge-Centered Systems focuses on data management, knowledge discovery, and knowledge processing, which are core and hot topics in computer science. Since the 1990s, the Internet has become the main driving force behind application development in all domains. An increase in the demand for resource sharing across different sites connected through networks has led to an evolution of data- and knowledge-management systems from centralized systems to decentralized systems enabling large-scale distributed applications providing high scalability. Current decentralized systems still focus on data and knowledge as their main resource. Feasibility of these systems relies basically on P2P (peer-to-peer) techniques and the support of agent systems with scaling and decentralized control. Synergy between grids, P2P systems, and agent technologies is the key to data- and knowledge-centered systems in large-scale environments. This, the eighth issue of Transactions on Large-Scale Data- and Knowledge-Centered Systems, contains eight revised selected regular papers focusing on the following topics: scalable data warehousing via MapReduce, extended OLAP multidimensional models, naive OLAP engines and their optimization, advanced data stream processing and mining, semi-supervised learning of data streams, incremental pattern mining over data streams, association rule mining over data streams, frequent pattern discovery over data streams.

[1]  Richard Sproat,et al.  Mining correlated bursty topic patterns from coordinated text streams , 2007, KDD '07.

[2]  Dimitrios Gunopulos,et al.  Identifying similarities, periodicities and bursts for online search queries , 2004, SIGMOD '04.

[3]  Divesh Srivastava,et al.  What's on the grapevine? , 2009, SIGMOD Conference.

[4]  Jure Leskovec,et al.  Meme-tracking and the dynamics of the news cycle , 2009, KDD.

[5]  Yan Jia,et al.  Online Burst Detection Over High Speed Short Text Streams , 2007, International Conference on Computational Science.

[6]  Ricardo Vilalta,et al.  A Decomposition of Classes via Clustering to Explain and Improve Naive Bayes , 2003, ECML.

[7]  Alfredo Cuzzocrea.  Retrieving Accurate Estimates to OLAP Queries over Uncertain and Imprecise Multidimensional Data Streams , 2011, SSDBM.

[8]  Christos Faloutsos,et al.  Data mining meets performance evaluation: fast algorithms for modeling bursty traffic , 2002, Proceedings 18th International Conference on Data Engineering.

[9]  Daphne Koller,et al.  Support Vector Machine Active Learning with Applications to Text Classification , 2000, J. Mach. Learn. Res..

[10]  Alfredo Cuzzocrea,et al.  CAMS: OLAPing Multidimensional Data Streams Efficiently , 2009, DaWaK.

[11]  Zhi-Li Zhang,et al.  Reducing Unwanted Traffic in a Backbone Network , 2005, SRUTI.

[12]  Philip S. Yu,et al.  Mining concept-drifting data streams using ensemble classifiers , 2003, KDD '03.

[13]  Jan Beran,et al.  Statistics for long-memory processes , 1994 .

[14]  Ravi Kumar,et al.  On the Bursty Evolution of Blogspace , 2003, WWW '03.

[15]  Walter Willinger,et al.  Analysis, modeling and generation of self-similar VBR video traffic , 1994, SIGCOMM.

[16]  Nick Koudas,et al.  TwitterMonitor: trend detection over the twitter stream , 2010, SIGMOD Conference.

[17]  Yan Jia,et al.  Counting Data Stream Based on Improved Counting Bloom Filter , 2008, 2008 The Ninth International Conference on Web-Age Information Management.

[18]  Xin Zhang,et al.  Better Burst Detection , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[19]  Ronald L. Rivest,et al.  Introduction to Algorithms, third edition , 2009 .

[20]  Tian Zhang,et al.  BIRCH: an efficient data clustering method for very large databases , 1996, SIGMOD '96.

[21]  Sharma Chakravarthy,et al.  Event-based lossy compression for effective and efficient OLAP over data streams , 2010, Data Knowl. Eng..

[22]  Claire Cardie,et al.  Proceedings of the Eighteenth International Conference on Machine Learning, 2001, p. 577–584. Constrained K-means Clustering with Background Knowledge , 2022 .

[23]  Yong Guan,et al.  Detecting Click Fraud in Pay-Per-Click Streams of Online Advertising Networks , 2008, 2008 The 28th International Conference on Distributed Computing Systems.

[24]  Aoying Zhou,et al.  Tracking clusters in evolving data streams over sliding windows , 2008, Knowledge and Information Systems.

[25]  Paul Barford,et al.  Generating representative Web workloads for network and server performance evaluation , 1998, SIGMETRICS '98/PERFORMANCE '98.