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The “Industrial Internet of things” (IIoT) has rapidly become a popular concept. However, the IIoT concept has caused problems in managing consistent identifiers. To solve these problems in control system management, we propose Structured ID, which can define the complicated structure of control systems using IDs on a tree structure, and relational links, which can describe meaning for subsets of structured IDs. Furthermore, in use cases that utilize consistently managed information, a method of distributing appropriate information to each stakeholder is required. To address this requirement, we propose an orthogonal information retrieval technique for Structured ID. Finally, we evaluate that the proposed techniques can be implemented using web technology and confirmed that those can solve problems in the Industrial Internet of things use case.

Increasing synchronization performance is one of the most important factors for improving Java performance. We present the concept of quasi-immutable objects and its application to implement fast synchronization primitives. Quasi-immutable objects are usually used for read accesses, but they are rarely used for write accesses. Using these characteristics, we describe the optimization for a synchronized read sequence of quasi-immutable objects. Our described method dynamically detects objects that are not written from critical sections of other threads, and applies the fast read sequence. The optimized read sequence enables the simultaneous execution of critical sections by multiple threads and reduces synchronization overhead. We implemented the described optimization on HotSpot Java VM. The experimental evaluations indicated that the optimization improved the performance of a micro-benchmark up to a factor of 16 and SPEC benchmarks up to 31%. Copyright © 2007 John Wiley & Sons, Ltd.

Efficient store and query of RDF graph database is of increasing importance due to the popularity and widespread acceptance of RDF on various applications including semantic/situation aware computing. In this paper, we have applied an query optimization based on graph contraction that boosts the query processing on relationally-backed RDF store. The query optimization technique based on graph contraction creates summarized graph in advance and uses it to efficiently query the original dataset. We used decomposition storage model on top of RDBMS to represent RDF graph, and applied the optimization technique based on graph contraction. Preliminary experiments using MySQL and synthetic dataset showed that the method can improve the query performance by 6.62 times. This means that the contraction based optimization on RDBMS is promising technique for retrieving data in semantic/situation aware computing.

This paper describes a parallel associative processor, lXM2, developed mainly for semantic network processing. IXM2 consists of 64 associative processors and 9 network processors, having a total of 256K words of associative memory. The large associative memory enables 65,536 semantic network nodes to be processed in parallel and reduces the order of algorithmic complexity to O( 1) in,basic semantic net operations. It is shown that IXM2 has computing power comparable to that of a Connection Machine. Programming for lXM2 is performed with the knowledge representation language IXL, a superset of Prolog, so that IXM2 can be utilized as a back-end to AI workstations.

The pseudo-vector processing (PVP) is a framework that enables fast processing similar to vector processing. In this paper, we describe the compiler optimizations that effectively utilize PVP on the SR8000. These include access method analysis, preloading, and prefetching optimizations. Evaluations on the SR8000 indicate that PVP can effectively hides memory latency.