scholarly journals Continuous Post-Mining of Association Rules in a Data Stream Management System

2009 ◽  
pp. 116-132
Author(s):  
Hetal Thakkar ◽  
Barzan Mozafari ◽  
Carlo Zaniolo
Keyword(s):  
Data Stream ◽  
Data Cleaning ◽  
Frequent Patterns ◽  
Data Stream Management ◽  
Efficient System ◽  
Service Guarantees ◽  
Stream Management ◽  
Continuous Mining ◽  
The Many

The real-time (or just-on-time) requirement associated with online association rule mining implies the need to expedite the analysis and validation of the many candidate rules, which are typically created from the discovered frequent patterns. Moreover, the mining process, from data cleaning to post-mining, can no longer be structured as a sequence of steps performed by the analyst, but must be streamlined into a workflow supported by an efficient system providing quality of service guarantees that are expected from modern Data Stream Management Systems (DSMSs). This chapter describes the architecture and techniques used to achieve this advanced functionality in the Stream Mill Miner (SMM) prototype, an SQL-based DSMS designed to support continuous mining queries.

EFFICIENTLY MINING RECENT FREQUENT PATTERNS OVER ONLINE TRANSACTIONAL DATA STREAMS

2009 ◽  
Vol 19 (05) ◽  
pp. 707-725 ◽  
Author(s):  
HUI CHEN
Keyword(s):  
Data Mining ◽  
Data Stream ◽  
Frequent Patterns ◽  
Stream Data ◽  
Data Stream Management ◽  
Online Data ◽  
Stream Data Mining ◽  
Network Traffic Analysis ◽  
Stream Management ◽  
Performance Results

Recent emerging applications, such as network traffic analysis, web click stream mining, power consumption measurement, sensor network data analysis, and dynamic tracing of stock fluctuation, call for study of a new kind of data, stream data. Many data stream management systems, prototype systems and software components have been developed to manage the streams or extract knowledge from stream data. Mining frequent patterns is a foundational job for the methods of data mining and knowledge discovery. This paper proposes an algorithm for mining the recent frequent patterns over an online data stream. This method uses RFP-tree to store compactly the recent frequent patterns of a stream. The content of each transaction is incrementally updated into the pattern tree upon its arrival by scanning the stream only once. Moreover, the strategy of conservative computation and time decaying model are used to ensure the correctness of the mining results. Finally, the performance results of extensive simulation show that our work can reduce the average processing time of stream data element and it is superior to other analogous algorithms.

SSC: cloud-based data stream management in distributed environments

2016 ◽  
Vol 9 (3) ◽  
pp. 171
Author(s):  
Kok Leong Ong ◽  
Andrzej Goscinski ◽  
Yuzhang Han ◽  
Peter Brezany ◽  
Zahir Tari ◽  
...  
Keyword(s):  
Data Stream ◽  
Distributed Environments ◽  
Data Stream Management ◽  
Stream Management

Data Stream Management Systems and Architectures

2007 ◽  
pp. 51-71 ◽  
Author(s):  
M. A. Hammad ◽  
T. M. Ghanem ◽  
W. G. Aref ◽  
A. K. Elmagarmid ◽  
M. F. Mokbel
Keyword(s):  
Data Stream ◽  
Management Systems ◽  
Data Stream Management ◽  
Stream Management ◽  
Data Stream Management Systems

Challenges for Personal Data Stream Management in Smart Buildings

2014 ◽  
pp. 201-219 ◽  
Author(s):  
Dennis Geesen ◽  
H. Jürgen Appelrath ◽  
Marco Grawunder ◽  
Daniela Nicklas
Keyword(s):  
Data Processing ◽  
Data Stream ◽  
Personal Data ◽  
Smart Homes ◽  
Property Management ◽  
Sensors And Actuators ◽  
Data Stream Management ◽  
Smart Building ◽  
Multiple Sensors ◽  
Stream Management

Smart homes are equipped with multiple sensors and actuators to observe the residents and environmental phenomena, to interpret the situation out of that, and finally, to react accordingly. While the data processing for a single smart home is facile, the data processing for multiple smart homes in one smart building is more complex because there are different people (e.g., like several residents, administrators, or a property management) with different interests concerning the processed data. On that point, this chapter shows which kind of typical roles can be found in a smart building and what requirements and challenges they demand for managing and processing the data. Secondly, Data Stream Management Systems (DSMS) are introduced as an approach for processing and managing data in a smart building by presenting an appropriate architecture. Finally, the chapter discusses further concepts from DSMS and illustrates how they additionally meet and solve the requirements and the challenges.

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