Performance Test Automation with Distributed Database Systems

Our previous research paper 2018;A Focus on Testing Issues in Distributed Database Systems' led us to a conclusion that Distributed Database Systems supports many good engineering practices but there is still place for refinements. A Distributed Database (DDB) is formed by a collection of multiple databases logically inter-related in a Computer Network. Apart from managing a plethora of complicated tasks, database management systems also need to be efficient in terms of concurrency, reliability, fault-tolerance and performance. As there has been a paradigm shift from centralized databases to Distributed databases, any testing process, when used in DDB correlates a series of stages for the construction of a DDB project right from the scratch and is employed in homogeneous systems. In this paper, an attempt is made to describe the establishment of Performance Testing with DDB systems. It focuses on the need for maintaining performance and some techniques to achieve performance in DDB systems. Three sample web based systems are tested by using TestMaker, one of the open source software, in order to highlight the helpful role of performance in the context of testing. The strengths and weaknesses of chosen performance testing tools viz., TestMaker, OpenSTA, and httperf are discussed

Heterogeneous Relational Databases for a Grid-enabled Analysis Environment

Grid based systems require a database access mechanism that can provide seamless homogeneous access to the requested data through a virtual data access system, i.e. a system which can take care of tracking the data that is stored in geographically distributed heterogeneous databases. This system should provide an integrated view of the data that is stored in the different repositories by using a virtual data access mechanism, i.e. a mechanism which can hide the heterogeneity of the backend databases from the client applications. This paper focuses on accessing data stored in disparate relational databases through a web service interface, and exploits the features of a Data Warehouse and Data Marts. We present a middleware that enables applications to access data stored in geographically distributed relational databases without being aware of their physical locations and underlying schema. A web service interface is provided to enable applications to access this middleware in a language and platform independent way. A prototype implementation was created based on Clarens [4], Unity [7] and POOL [8]. This ability to access the data stored in the distributed relational databases transparently is likely to be a very powerful one for Grid users, especially the scientific community wishing to collate and analyze data distributed over the Grid

The Internet of Things as a Privacy-Aware Database Machine

Instead of using a computer cluster with homogeneous nodes and very fast high bandwidth connections, we want to present the vision to use the Internet of Things (IoT) as a database machine. This is among others a key factor for smart (assistive) systems in apartments (AAL, ambient assisted living), offices (AAW, ambient assisted working), Smart Cities as well as factories (IIoT, Industry 4.0). It is important to massively distribute the calculation of analysis results on sensor nodes and other low-resource appliances in the environment, not only for reasons of performance, but also for reasons of privacy and protection of corporate knowledge. Thus, functions crucial for assistive systems, such as situation, activity, and intention recognition, are to be automatically transformed not only in database queries, but also in local nodes of lower performance. From a database-specific perspective, analysis operations on large quantities of distributed sensor data, currently based on classical big-data techniques and executed on large, homogeneously equipped parallel computers have to be automatically transformed to billions of processors with energy and capacity restrictions. In this visionary paper, we will focus on the database-specific perspective and the fundamental research questions in the underlying database theory