Hideaki Hirayama

Goal achievement analysis based on LTL checking and decision tree for improvements of PAIS

Process aware information system (PAIS) is important in the recent business environment. Developments of PAIS need to consider contexts about technical and business elements. They are needed to develop PAIS effectively (e.g. monitoring environment and constructing adequate business process). Process mining is an important method for analyzing a business environment and utilizing PAIS development and improvement. LTL checking is an important method for checking a specific property to be satisfied with business processes, but correctly writing formal language like LTL is difficult. In this paper, we use LTL checking and prediction based on decision-tree learning for checking goal achievement, false detection and oversight detection. It helps writing properly LTL formula for representing the correct goal property. We conducted a case study using a real life log of traffic fine management process in Italy.

Process Mining Approach Based on Partial Structures of Event Logs and Decision Tree Learning

Process mining techniques are able to improve processes by extracting knowledge from event logs commonly available in todays information systems. In the area, it is important to verify whether business goals can be satisfied. LTL (Linear Temporal Logic) verification is an important means for checking the goals automatically and exhaustively. However, writing formal language like LTL is difficult, and the properties by which the users intentions are not reflected sufficiently have bad influence on the verification results. Therefore, it is needed to help writing correct LTL formula for users who do not have sufficient domain knowledge and knowledge of mathematical logic. We propose an approach for goal achievement prediction based on decision tree learning. It is conducted focusing on partial structures represented as event order relations of each trace. The proposed technique is evaluated on a phone repair process log.

Business Process Verification and Restructuring LTL Formula Based on Machine Learning Approach

It is important to deal with rapidly changing environments (regulations, customer behavior change, and process improvement etc.) to keep achieving business goals. Therefore, verification for business process in various phases are needed to make sure of goal achievements. LTL (Linear Temporal Logic) verification is an important method for checking a specific property to be satisfied with business processes, but correctly writing formal language like LTL is difficult. Lacks of domain knowledge and knowledge of mathematical logics have bad influence on writing LTL formulas. In this paper, we use LTL verification and prediction based on decision tree learning for verification of specific properties. Furthermore, we helps writing properly LTL formula for representing the correct desirable property using decision tree constrction. We conducted a case study for evaluations.

Distributed shared memory with log based consistency for operations with commutative law or associative law

With progress in high-performance networks, cluster systems with high-performance and inexpensive work stations or personal computers are attracting much attention. For the cluster systems to become popular, it is necessary that it be easy to develop programs on them. Distributed Shared Memory is the key to achieving this objective. But Distributed Shared Memory cannot achieve high performance for all application programs. In particular, programs which frequently modify the same fields by multiple nodes cannot attain high performance with Distributed Shared Memory. In this paper, we propose Distributed Shared Memory with Log Based Consistency for such application programs, such as aggregation applications in the business application area. In this scheme, consistency is maintained by transferring logs among multiple nodes for operations with the commutative law or associative law. Distributed Shared Memory with Log Based Consistency can achieve much better performance than the traditional Distributed Shared Memory. Its performance is almost the same as that of SMP parallel computers.

Scalable data mining with log based consistency DSM for high performance distributed computing

Mining the large Web based online distributed databases to discover new knowledge and financial gain is an important research problem. These computations require high performance distributed and parallel computing environments. Traditional data mining techniques such as classification, association, clustering can be extended to find new efficient solutions. The paper presents the scalable data mining problem, proposes the use of software DSM (distributed shared memory) with a new mechanism as an effective solution and discusses both the implementation and performance evaluation results. It is observed that the overhead of a software DSM is very large for scalable data mining programs. A new Log Based Consistency (LBC) mechanism, especially designed for scalable data mining on the software DSM is proposed to overcome this overhead. Traditional association rule based data mining programs frequently modify the same fields by count-up operations. In contrast, the LBC mechanism keeps up the consistency by broadcasting the count-up operation logs among the multiple nodes.

Distributed shared memory with log based consistency for scalable data mining

The paper presents the scalable data mining problem, proposes the use of software DSM (Distributed Shared Memory) with a new mechanism as an effective solution and discusses both the implementation and performance evaluation results. It is observed that the overhead of a software DSM is very large for scalable data mining programs. A new Log Based Consistency (LBC) mechanism, especially designed for scalable data mining on the software DSM is proposed to overcome this overhead. Traditional association rule based data mining programs frequently modify the same fields by count-up operations. In contrast, the LBC mechanism keeps up the consistency by broadcasting the count-up operation logs among the multiple nodes.