In-memory Databases in Business Information Systems

In-memory databases are developed to keep the entire data in main memory. Compared to traditional database systems, read access is now much faster since no I/O access to a hard drive is required. In terms of write access, mechanisms are available which provide data persistence and thus secure transactions. In-memory databases have been available for a while and have proven to be suitable for particular use cases. With increasing storage density of DRAM modules, hardware systems capable of storing very large amounts of data have become affordable. In this context the question arises whether in-memory databases are suitable for business information system applications. Hasso Plattner, who developed the HANA in-memory database, is a trailblazer for this approach. He sees a lot of potential for novel concepts concerning the development of business information systems. One example is to conduct transactions and analytics in parallel and on the same database, i.e. a division into operational database systems and data warehouse systems is no longer necessary (Plattner and Zeier 2011). However, there are also voices against this approach. Larry Ellison described the idea of business information systems based on in-memory database as “wacko,” without actually making a case for his statement (cf. Bube 2010). Stonebraker (2011) sees a future for in-memory databases for business information systems but considers the division of OLTP and OLAP applications as reasonable. [From: Introduction

The Information Panopticon in the Big Data Era

Taking advantage of big data opportunities is challenging for traditional organizations. In this article, we take a panoptic view of big data – obtaining information from many sources and making it visible to all organizational levels. We suggest that big data requires the transformation from command and control hierarchies to post-bureaucratic organizational structures wherein employees at all levels can be empowered while simultaneously being controlled. We derive propositions that show how to best exploit big data technologies in organizations

LEVERAGING IN-MEMORY TECHNOLOGY TO IMPROVE THE ACCEPTANCE OF MSS - A MANAGERS´ PERSPECTIVE

Management support systems (MSS) help managers to perform their jobs more efficiently. With in-memory technology, a new IT enabler promises to support managers by benefits ranging from reducing time for MSS data entry and analysis to completing even new topics of analysis. Hence, the present situation is favorable for an MSS redesign applying in-memory apps. Such apps are field-tested and ready-to-use, but from a business perspective they lack impact. Based on findings from a literature review and results from a workshop with an expert focus group validated with one-on-one manager interviews, we propose four initial use situations in which in-memory apps contribute to greater MSS acceptance: (1) In-memory apps should accelerate the MSS response time for both check status and receive an alert. In doing so, they should focus on information from management accounting. (2) By delivering information more timely, in-memory apps should contribute to MSS standard reports and financial closing. (3) In-memory apps should accelerate MSS response time for both ad-hoc analysis and drill-down/drill-through analysis. (4) Leveraging in-memory apps, MSS ad-hoc analysis and drill down/drill-through analysis should become more flexible.

In-Memory and Column Storage Changes IS Curriculum

Random Access Memory (RAM) prices have been dropping precipitously. This has given rise to the possibility of keeping all data gathered in RAM rather than utilizing disk storage. This technological capability, along with benefits associated with a columnar storage database system, reduces the benefit of Relational Database Management Systems (RDBMS) and eliminates the need for Online Transactional Processing (OLTP) and Online Analytical Processing (OLAP) activities to remain separate. The RDBMS was a required data structure due to the need to separate the daily OLTP activities from the OLAP analysis of that data. In-memory processing allows both activities simultaneously. Data analysis can be done at the speed of data capture. Relational databases are not the only option for organizations. In-Memory is emerging, and university curriculum needs to innovate and create skills associated with denormalization of existing database (legacy) systems to prepare for the next generation of data managers

A Process-Oriented Model to Business Value – the Case of Real-Time IT Infrastructures

Which investments in real-time capabilities and decision-support IT-infrastructures are appropriate? In view of the recent in-memory systems this poses an urgent question to companies in many industries. Despite ample research on the causal relationship between IS investments and business value, especially the value quantification remains a difficult challenge. This paper contributes a business value measurement model that structures and assesses the internal organizational benefits of real-time IT infrastructures. A case study from the automotive industry aims to validate the model

Towards Next Generation Business Process Model Repositories – A Technical Perspective on Loading and Processing of Process Models

Business process management repositories manage large collections of process models ranging in the thousands. Additionally, they provide management functions like e.g. mining, querying, merging and variants management for process models. However, most current business process management repositories are built on top of relation database management systems (RDBMS) although this leads to performance issues. These issues result from the relational algebra, the mismatch between relational tables and object oriented programming (impedance mismatch) as well as new technological developments in the last 30 years as e.g. more and cheap disk and memory space, clusters and clouds. The goal of this paper is to present current paradigms to overcome the performance problems inherent in RDBMS. Therefore, we have to fuse research about data modeling along database technologies as well as algorithm design and parallelization for the technology paradigms occurring nowadays. Based on these research streams we have shown how the performance of business process management repositories could be improved in terms of loading performance of processes (from e.g. a disk) and the computation of management techniques resulting in even faster application of such a technique. Exemplarily, applications of the compiled paradigms are presented to show their applicability

IS THERE STILL A NEED FOR MULTIDIMENSIONAL DATA MODELS?

Organizational and technical changes challenge standards of data warehouse design and initiate a redesign of contemporary Business Intelligence and Analytics environments. As a result, the use of multidimensional models for performance oriented reasons is not necessarily taken for granted. Simple data models or operational structures emerge as a basis for complex analyses. The paper therefore conducts a laboratory experiment to examine from a non-technical perspective the influnce of different data modeling types on the representational information quality of end users. A comparison is made between the multidimensional model and the transactional model respectively the flat file model. The experiment involves 78 participants and aims to compare perceived and observed representational information quality aspects of ad hoc analyses regarding the data modeling type. The results indicate a higher observed quality for multidimensional modeled data, while different types of data models do not influnce the end user perception of the representational information quality

In-memory business intelligence: a Wits context

The organisational demand for real-time, flexible and cheaper approaches to Business Intelligence is impacting the Business Intelligence ecosystem. In-memory databases, in-memory analytics, the availability of 64 bit computing power, as well as the reduced costs of memory, are enabling technologies to meet this demand. This research report examines whether these technologies will have an evolutionary or a revolutionary impact on traditional Business Intelligence implementations. An in-memory analytic solution was developed for University of the Witwatersrand Procurement Office, to evaluate the benefits claimed for the in-memory approach for Business intelligence, in the development, reporting and analysis processes. A survey was used to collect data on the users' experience when using an in-memory solution. The results indicate that the in-memory solution offers a fast, flexible and visually rich user experience. However, there are certain key steps of the traditional BI approach that cannot be omitted. The conclusion reached is that the in-memory approach to Business Intelligence can co-exist with the traditional Business Intelligence approach, so that the merits of both approaches can be leveraged to enhance value for an organisation