An efficient spatial high-utility occupancy frequent item mining algorithm for mission system integration architecture design using the MBSE method

One of the major issues in system integration is to deal with interoperability of legacy systems which use traditional System Integration Patterns (SIP). Information are unable to exchange effectively when the systems involved comes from developer that tended to not interoperate and this leads to the interoperability problem in heterogeneous system integration. To address the interoperability issues, interfacing processes need to be made more easily by defining components, processes, and interfaces that affect the system integration architecture at the initial design stage. This paper includes a basic concept on types of traditional SIP covering File-Based, Common Database, Remote Procedure Call (RPC), Distributed Objects, and Messaging. An overview of three Service Interface Design (SID) approaches for systems interoperability is discussed. The discussions on these approaches serve as a basis for the solution of interoperability of heterogeneous systems which use traditional SIP.

Download Full-text

Recent Frequent Item Mining Algorithm in a Data Stream Based on Flexible Counter Windows

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.385-386.1415 ◽

2013 ◽

Vol 385-386 ◽

pp. 1415-1418

Author(s):

Yan Yang Guo ◽

Gang Wang ◽

Feng Mei Hou ◽

Qing Ling Mei

Keyword(s):

Data Stream ◽

Variable Length ◽

Streaming Data ◽

Frequent Item ◽

Mining Algorithm ◽

Variable Window

In the paper the author introduces FCW_MRFI, which is a streaming data frequent item mining algorithm based on variable window. The FCW_MRFI algorithm can mine frequent item in any window of recent streaming data, whose given length is L. Meanwhile, it divides recent streaming data into several windows of variable length according to m, which is the number of the counter array. This algorithm can achieve smaller query error in recent windows, and can minimize the maximum query error in the whole recent streaming data.

Download Full-text

The Research of Improved Apriori Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.263-266.2179 ◽

2012 ◽

Vol 263-266 ◽

pp. 2179-2184 ◽

Cited By ~ 4

Author(s):

Zhen Yun Liao ◽

Xiu Fen Fu ◽

Ya Guang Wang

Keyword(s):

Association Rule ◽

Association Rule Mining ◽

Apriori Algorithm ◽

Rule Mining ◽

Frequent Item ◽

Mining Algorithm ◽

The Times ◽

Frequent Item Sets ◽

Candidate Item ◽

Improved Algorithm

The first step of the association rule mining algorithm Apriori generate a lot of candidate item sets which are not frequent item sets, and all of these item sets cost a lot of system spending. To solve this problem，this paper presents an improved algorithm based on Apriori algorithm to improve the Apriori pruning step. Using this method, the large number of useless candidate item sets can be reduced effectively and it can also reduce the times of judge whether the item sets are frequent item sets. Experimental results show that the improved algorithm has better efficiency than classic Apriori algorithm.

Download Full-text