The manufacturing business field is accommodating a massive number of networked sensors, which are offering a new horizon of microscopic observability; every single piece of the production line is enabled to be monitored, transformed and organized into collective digital assets. Intensive analytics on the digital assets potentially offers new business solutions to improve the manufacturing productivity and secure the business continuity. For example, the capability of exhaustive production inspection would potentially remove the delivery of defective products. Another scenario is the capability of expeditiously identifying an impaired part in the production line, which would significantly reduce the yield loss. These business analytics work often hold the nature that they need to precisely identify a particular manufacturing event of interest (e.g., producing an intermediate material at a suspiciously faulty machine) and then trace back or forward every ancestor or descendant of the event thoroughly in the manufacturing pipeline spanning from the initial raw material to the final product. Yet, such microscopic business traceability has not been actively addressed in the technology community. This paper presents our experimental exploration of how to design relational database for manufacturing business analytics, which is technically differentiated by the involvement of microscopic business traceability from traditional business analytics. We have performed intensive experiments and cost-performance analyses to figure out the trade-offs associated with major options of physical database design with different benchmarks on a commercial database management system (DBMS) in an enterprise environment. The combination of columnar table stores and secondary indexes is recommended for manufacturing business analytics workloads, even though the pure use of columnar table stores is recommended for traditional analytics workloads. We believe that our experience would help those planning to work on microscopic business tractability in manufacturing and other fields.

To improve business efficiency, it has become important to utilize data from multiple databases adhering to complex regulations. For efficient data utilization across multiple databases, the data analyst needs to prepare distributed queries in compliance with the complex regulations imposed by countries and organizations respectively. Before the prepared distributed query can be executed, it needs to go through compliance check for country and organizational regulations. Existing approaches for compliance check are inefficient as they are applicable for databases considering only one type of regulation. In this paper, we propose an efficient hierarchical compliance check method which is applicable for multiple databases adhering to regulation by country and organization. To improve efficiency a combination of country regulation checker and organization regulation checker is developed. The method first checks regulations imposed by country and then checks regulations imposed by each organization resulting in hierarchical combination. We show that our proposed compliance check method takes approximately 36.7% less time to obtain compliance status. Obtaining compliance status quickly results in increasing the interactivity of the data analyst for data utilization from multiple databases.

Rapidly escalating energy and cooling costs of storage systems have become a concern for data centers. In response, a multitude of energy saving approaches that take into account storage-device-level input/output (I/O) behaviors has been proposed. The trouble is that critical applications are in constant operation at data centers, and the conventional approaches do not produce sufficient energy savings. It may be possible to dramatically reduce storage energy consumption without degrading application performance levels by utilizing application level I/O behaviors. However, such behaviors differ from one application to another, and it would be too expensive to tailor methods to individual applications. We propose a universal storage energy management framework for runtime storage energy savings that can be applied to any type of application. The results of evaluations show that the use of this framework results in substantive energy savings compared with the traditional approaches.

Rapidly escalating energy and cooling costs, especially those related to the energy consumption of storage systems, have become a concern for data centers, primarily because the amount of digital data that needs storage is increasing daily. In response, a multitude of energy saving approaches that take into account storage-device-level input/output (I/O) behaviors have been proposed. The trouble is that numerous critical applications such as database systems or web commerce applications are in constant operation at data centers, and the conventional approaches that only utilize storage-device-level I/O behaviors do not produce sufficient energy savings. It may be possible to dramatically reduce storage-related energy consumption without degrading application performance levels by utilizing application-level I/O behaviors. However, such behaviors differ from one application to another, and it would be too expensive to tailor methods to individual applications. As a way of solving this problem, we propose a universal storage energy management framework for runtime storage energy savings that can be applied to any type of application. The results of evaluations show that the use of this framework results in substantive energy savings compared with the traditional approaches that are used while applications are running.

Power, especially that consumed for storing data, and cooling costs for data centers have increased rapidly. The main applications running at data centers are data intensive applications such as large file servers or database systems. Recently, power management of the data intensive applications has been emphasized in the literature. Such reports discuss the importance of power savings. However, these reports lack research on power management models for the efficient use of data intensive applications' I/O behaviors. This paper proposes a novel energy efficient storage management system that monitors both application- and device-level I/O patterns at run time, and uses not only the device-level I/O pattern but also application level patterns. First, the design of the proposed model combined with such large data intensive applications will be shown. The key features of the model are i) classifying application-level I/O into four patterns using run-time access behaviors such as the length of idle time and read/write frequency, and ii) adopting an appropriate power-saving method-based on these application level I/O patterns. Next, the proposed method is quantitatively evaluated with typical data intensive applications such as file servers, OLTP, and DSS. It is shown that energy efficient storage management is effective in achieving large power savings compared with traditional approaches while an application is running.