数据挖掘 Renmin_Data Mining 8.1Stream.ppt

Data Mining: Principles and Algorithms Data Mining:  Principles and Research Frontiers — Chapter 8.1 — — Stream Data Mining — ?Jiawei Han Department of Computer Science University of Illinois at Urbana-Champaign /~hanj Stream Data Mining What is stream data? Why Stream Data Systems? Stream data management systems: Issues and solutions Stream data cube and multidimensional OLAP analysis Stream frequent pattern analysis Stream classification Stream cluster analysis Research issues Stream Data Mining What is stream data? Why Stream Data Systems? Stream data management systems: Issues and solutions Stream data cube and multidimensional OLAP analysis Stream frequent pattern analysis Stream classification Stream cluster analysis Research issues Characteristics of Data Streams Data Streams Data streams—continuous, ordered, changing, fast, huge amount Traditional DBMS—data stored in finite, persistent data sets Characteristics Huge volumes of continuous data, possibly infinite Fast changing and requires fast, real-time response Data stream captures nicely our data processing needs of today Random access is expensive—single linear scan algorithm (can only have one look) Store only the summary of the data seen thus far Most stream data are at pretty low-level or multi-dimensional in nature, needs multi-level and multi-dimensional processing Stream Data Applications Telecommunication calling records Business: credit card transaction flows Network monitoring and traffic engineering Financial market: stock exchange Engineering industrial processes: power supply manufacturing Sensor, monitoring surveillance: video streams, RFIDs Security monitoring Web logs and Web page click streams Massive data sets (even saved but random access is too expensive) DBMS versus DSMS Persistent relations One-time queries Random access “Unbounded” disk store Only current state matters No real-time services Relatively low update rate Data at any granularity Assume precise data Access plan