Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation

Recent Advances in Social Data and Artificial Intelligence 2019

The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace

Accelerating Itemset Sampling using Satisfiability Constraints on FPGA

International audienceFinding recurrent patterns within a data stream is important for fields as diverse as cybersecurity or e-commerce. This requires to use pattern mining techniques. However, pattern mining suffers from two issues. The first one, known as “pattern explosion”, comes from the large combinatorial space explored and is the result of too many patterns outputed to be analyzed. Recent techniques called output space sampling solve this problem by outputing only a sampled set of all the results, with a target size provided by the user. The second issue is that most algorithms are designed to operate on static datasets or low throughput streams. In this paper, we propose a contribution to tackle both issues, by designing an FPGA accelerator for pattern mining with output space sampling. We show that our accelerator can outperform a state-of-the-art implementation on a server class CPU using a modest FPGA product