Non-volatile memory (NVM) is a new storage technology that combines the performance and byte addressability of DRAM with the persistence of traditional storage devices like flash (SSD). While these properties make NVM highly promising, it is not yet clear how to best integrate NVM into the storage layer of modern database systems. Two system designs have been proposed. The first is to use NVM exclusively, i.e., to store all data and index structures on it. However, because NVM has a higher latency than DRAM, this design can be less efficient than main-memory database systems. For this reason, the second approach uses a page-based DRAM cache in front of NVM. This approach, however, does not utilize the byte addressability of NVM and, as a result, accessing an uncached tuple on NVM requires retrieving an entire page.

Next generation sequencing (NGS) and the recent development of efficient algorithms for genomic analysis are contributing to the understanding of human genetic variation and thus to personalized medicine. Among those genomic analysis, disease-causal gene analysis that finds genes relevant to specific diseases has received much attention. In this paper, we present our work on extending the PostgreSQL open source relational database management system (RDBMS) to efficiently handle genomic analysis. We introduced a new genome data type and a genome type aggregation function that drastically improved the performance of a typical query for disease-causal gene analysis by a factor of 50 to 360.

Good performance and efficiency, in terms of high quality of service and resource utilization for example, are important goals in a cloud environment. Through extensive measurements of an n-tier application benchmark (RUBBoS), we show that overall system performance is surprisingly sensitive to appropriate allocation of soft resources (e.g., server thread pool size). Inappropriate soft resource allocation can quickly degrade overall application performance significantly. Concretely, both under-allocation and over-allocation of thread pool can lead to bottlenecks in other resources because of non-trivial dependencies. We have observed some non-obvious phenomena due to these correlated bottlenecks. For instance, the number of threads in the Apache web server can limit the total useful throughput, causing the CPU utilization of the C-JDBC clustering middleware to decrease as the workload increases. We provide a practical iterative solution approach to this challenge through an algorithmic combination of operational queuing laws and measurement data. Our results show that soft resource allocation plays a central role in the performance scalability of complex systems such as n-tier applications in cloud environments.

In this paper we present our experience in applying Event Analyzer, a processing engine we have developed to extract patterns from a sequence of events, in the checking of medical orders of a CPOE system. We present some extensions we have implemented in Event Analyzer in order to fulfill the needs of those orders checking, as well as some performance evaluation results. We also outline some problems we are facing now to adapt Event Analyzer's pattern detection engine to support streaming orders in an on-line CPOE checking system.

In this paper we present a tool called Event Analyzer that processes events that compose a sequence. We present the data model in which Event Analyzer is based, as well as its query language that allows the expression of complex patterns to be searched over the sequence of events. The Event Analyzer has been developed and it now integrates the Fujitsu Symfoware e-Business Intelligence Suite Premium.

Recently a growing number of applications monitor the physical world by tracking sensor data and detecting values, trends or patterns of interest. We focus on the problem of detecting sequential patterns with complex predicates over sensor data, and present an algorithm that efficiently pre-computes which pattern predicates' checks can be skipped at query compile-time, so that the processing window can slide with only necessary checks being actually performed against the sensor data at run-time. Implementation and evaluation of the proposed approach confirms its efficiency when compared to previously proposed approaches.

Article Dynamic skew handling in parallel mining of association rules Share on Authors: Lilian Harada Software Laboratory, Fujitsu Laboratories Ltd. Software Laboratory, Fujitsu Laboratories Ltd.View Profile , Naoki Akaboshi Software Laboratory, Fujitsu Laboratories Ltd. Software Laboratory, Fujitsu Laboratories Ltd.View Profile , Kazutaka Ogihara Software Laboratory, Fujitsu Laboratories Ltd. Software Laboratory, Fujitsu Laboratories Ltd.View Profile , Riichiro Take Software Laboratory, Fujitsu Laboratories Ltd. Software Laboratory, Fujitsu Laboratories Ltd.View Profile Authors Info & Claims CIKM '98: Proceedings of the seventh international conference on Information and knowledge managementNovember 1998 Pages 76–85https://doi.org/10.1145/288627.288634Published:01 November 1998 1citation276DownloadsMetricsTotal Citations1Total Downloads276Last 12 Months1Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Join algorithms on KD-tree indexed relations are proposed. The join algorithms are based on a concept called wave. The wave is a set of pages that is the object of joining and that propagates over the relation space in the direction of the join attribute axis. Four basic join algorithms that determine the wave from one of the relations and one algorithm that determines the wave from both relations are proposed. The algorithms are described and extensively analyzed with analytical formulas and simulation results. Then a garbage collection mechanism is introduced that discards the unnecessary data loaded in the main memory and extends the previous basic algorithms with an efficient memory management. It is shown that the proposed algorithms perform the join of very large relations with one scan.>

The major performance bottle neck in the current computer system is in the low-performance secondary system. The performance of the CPU has increased dramatically so far, about several orders of magnitude improvement has been achieved. On the other hand, that of the disk system has shown little advance since nineteen sixties. The von Neumann bottle neck between the CPU and the secondary storage subsystem has been much more enlarged.