Enhancing systems integration by incorporating business continuity drivers

Purpose – The purpose of this paper is to present a framework for developing an integrated operating environment (IOE) within an enterprise information system by incorporating business continuity drivers. These drivers enable a business to continue with its operations even if some sort of failure or disaster occurs. Design/methodology/approach – Development and implementation of the framework are based on holistic and top-down approach. An IOE on server’s side of contemporary business computing is investigated in depth. Findings – Key disconnection points are identified, where systems integration technologies can be used to integrate platforms, protocols, data and application formats, etc. Downtime points are also identified and explained. A thorough list of main business continuity drivers (continuous computing (CC) technologies) for enhancing business continuity is identified and presented. The framework can be utilized in developing an integrated server operating environment for enhancing business continuity. Originality/value – This paper presents a comprehensive framework including exhaustive handling of enabling drivers as well as disconnection points toward CC and business continuity

Storage Solutions for Big Data Systems: A Qualitative Study and Comparison

Big data systems development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems design include choosing appropriate storage and computing infrastructures. In this age of heterogeneous systems that integrate different technologies for optimized solution to a specific real world problem, big data system are not an exception to any such rule. As far as the storage aspect of any big data system is concerned, the primary facet in this regard is a storage infrastructure and NoSQL seems to be the right technology that fulfills its requirements. However, every big data application has variable data characteristics and thus, the corresponding data fits into a different data model. This paper presents feature and use case analysis and comparison of the four main data models namely document oriented, key value, graph and wide column. Moreover, a feature analysis of 80 NoSQL solutions has been provided, elaborating on the criteria and points that a developer must consider while making a possible choice. Typically, big data storage needs to communicate with the execution engine and other processing and visualization technologies to create a comprehensive solution. This brings forth second facet of big data storage, big data file formats, into picture. The second half of the research paper compares the advantages, shortcomings and possible use cases of available big data file formats for Hadoop, which is the foundation for most big data computing technologies. Decentralized storage and blockchain are seen as the next generation of big data storage and its challenges and future prospects have also been discussed

Exploratory Research on Developing Hadoop-based Data Analytics Tools

As we crossed past the millennium mark, slowly but steadily we were flooded by (digital) data from virtually everywhere. The challenges of big data demand new methods, techniques, and paradigms for processing data in a fast and scalable fashion. Most enterprises today need to employ data analysis technologies to remain competitive and profitable. Alas, the adoption of big data analysis technologies in Indonesia is still in its infancy, even in the academic sector. To encourage more adoptions of big data technologies, this study explored the development of Hadoop-based data analytics tools. Two case studies were used in the exploration. One is to showcase the performance comparison between Hadoop and DBMS, whereas the other is between Hadoop and a statistical analysis tool. Results clearly demonstrate that Hadoop is superior in processing a large data size. We also derive some recommendations to tune Hadoop optimally

Big Data Management Challenges, Approaches, Tools and their limitations

International audienceBig Data is the buzzword everyone talks about. Independently of the application domain, today there is a consensus about the V's characterizing Big Data: Volume, Variety, and Velocity. By focusing on Data Management issues and past experiences in the area of databases systems, this chapter examines the main challenges involved in the three V's of Big Data. Then it reviews the main characteristics of existing solutions for addressing each of the V's (e.g., NoSQL, parallel RDBMS, stream data management systems and complex event processing systems). Finally, it provides a classification of different functions offered by NewSQL systems and discusses their benefits and limitations for processing Big Data

Mobile Databases: a Selection of Open Issues and Research Directions

International audienceThis paper reports on the main results of a specific action on mobile databases conducted by CNRS in France from October 2001 to December 2002. The objective of this action was to review the state of progress in mobile databases and identify major research directions for the French database community. Rather than provide a survey of all important issues in mobile databases, this paper gives an outline of the directions in which the action participants are now engaged, namely: copy synchronization in disconnected computing, mobile transactions, database embedded in ultra-light devices, data confidentiality, P2P dissemination models and middleware adaptability