Business Intelligence for the Real-Time Enterprises 

About: Business Intelligence for the Real-Time Enterprises is an academic conference. The conference publishes majorly in the area(s): Data warehouse & Business intelligence. Over the lifetime, 87 publications have been published by the conference receiving 1031 citations.

A Hybrid Row-Column OLTP Database Architecture for Operational Reporting

Abstract: Operational reporting differs from informational reporting in that its scope is on day-to-day operations and thus requires data on the detail of individual transactions. It is often not desirable to maintain data on such detailed level in the data warehouse, due to both exploding size of the warehouse and the update frequency required for operational reports. Using an ODS as the source for operational reporting exhibits a similar information latency.

Pricing Approaches for Data Markets

Abstract: Currently, multiple data vendors utilize the cloud-computing paradigm for trading raw data, associated analytical services, and analytic results as a commodity good. We observe that these vendors often move the functionality of data warehouses to cloud-based platforms. On such platforms, vendors provide services for integrating and analyzing data from public and commercial data sources. We present insights from interviews with seven established vendors about their key challenges with regard to pricing strategies in different market situations and derive associated research problems for the business intelligence community.

Relax and Let the Database Do the Partitioning Online

Abstract: Vertical and Horizontal partitions allow database administrators (DBAs) to considerably improve the performance of business intelligence applications. However, finding and defining suitable horizontal and vertical partitions is a daunting task even for experienced DBAs. This is because the DBA has to understand the physical query execution plans for each query in the workload very well to make appropriate design decisions. To facilitate this process several algorithms and advisory tools have been developed over the past years. These tools, however, still keep the DBA in the loop. This means, the physical design cannot be changed without human intervention. This is problematic in situations where a skilled DBA is either not available or the workload changes over time, e.g. due to new DB applications, changed hardware, an increasing dataset size, or bursts in the query workload. In this paper, we present AutoStore: a self-tuning data store which rather than keeping the DBA in the loop, monitors the current workload and partitions the data automatically at checkpoint time intervals — without human intervention. This allows AutoStore to gradually adapt the partitions to best fit the observed query workload. In contrast to previous work, we express partitioning as a One-Dimensional Partitioning Problem (1DPP), with Horizontal (HPP) and Vertical Partitioning Problem (VPP) being just two variants of it. We provide an efficient \(\textsc{O}^2\) P (One-dimensional Online Partitioning) algorithm to solve 1DPP. \(\textsc{O}^2\) P is faster than the specialized affinity-based VPP algorithm by more than two orders of magnitude, and yet it does not loose much on partitioning quality. AutoStore is a part of the OctopusDB vision of a One Size Fits All Database System [13]. Our experimental results on TPC-H datasets show that AutoStore outperforms row and column layouts by up to a factor of 2.

Near Real-Time Data Warehousing Using State-of-the-Art ETL Tools

Abstract: Data warehouses are traditionally refreshed in a periodic manner, most often on a daily basis. Thus, there is some delay between a business transaction and its appearance in the data warehouse. The most recent data is trapped in the operational sources where it is unavailable for analysis. For timely decision making, today’s business users asks for ever fresher data.

Situational Business Intelligence

Abstract: Traditional business intelligence has focused on creating dimensional models and data warehouses, where after a high modeling and creation cost structurally similar queries are processed on a regular basis. So called "ad-hoc" queries aggregate data from one or several dimensional models, but fail to incorporate other external information that is not considered in the pre-defined data model. We focus on a different kind of business intelligence, which spontaneously correlates data from a company’s data warehouse with "external" information sources that may come from the corporate intranet, are acquired from some external vendor, or are derived from the internet. Such situational applications are usually short-lived programs created for a small group of users with a specific business need. We will showcase the state-of-the-art for situational applications as well as the impact of Web 2.0 for these applications. We will also present examples and research challenges that the information management research community needs to address in order to arrive at a platform for Situational Business Intelligence.