Modern in-memory database systems are facing the need of efficiently supporting mixed workloads of OLTP and OLAP. A conventional approach to this requirement is to rely on ETL-style, application-driven data replication between two very different OLTP and OLAP systems, sacrificing realtime reporting on operational data. An alternative approach is to run OLTP and OLAP workloads in a single machin...

در این مقاله یک مدل ریاضی برای مسئله سیستم تولیدی همکارانه ساخت بر اساس سفارش با رعایت انصاف تخصیص بار‌های تولید طراحی شده است. اهداف اصلی مدل، کمینه‌سازی هزینه‌‌های کل و حداکثر استفاده از منابع به‌منظور عادلانه شرایط عدم­قطعیت کنترل پارامتر‌های غیرقطعی روش برنامه‌ریزی فازی ‌شده نتایج نشان می‌دهد افزایش نرخ عدم‌قطعیت، می­یابد. ازآنجاکه ظرفیت کارخانه‌ها ثابت است، مقدار تقاضا، هر کارخانه نیز می­ی...

The healthcare industry collects huge amounts of data which, unfortunately, are not turned into useful information for effective decision making. Decision support systems (DSS) can now use advanced technologies such as On-Line Analytical Processing (OLAP) and data mining to deliver advanced capabilities. This paper presents a prototype clinical decision support system which combines the strengt...

Profiling customers’ behavior has become increasingly important for many applications such as fraud detection, targeted marketing and promotion. Customer behavior profiles are created from very large collections of transaction data. This has motivated us to develop a data-warehouse and OLAP based, scalable and flexible profiling engine. We define profiles by probability distributions, and compu...

eBISS 2013 Third European Business Intelligence Summer School Business model ontologies capture the complex interdependencies between business objects. The analysis of the hence formalized knowledge eludes traditional OLAP systems which operate on numeric measures. Many real-world facts, however, do not boil down to a single number but are more accurately represented by business model ontologie...

Version 8 of the SAS® System brings powerful new features for managing a Hybrid OLAP (HOLAP) or Distributed Multidimensional Data environment. The HOLAP component of the SAS/MDDB® Server software enables you to include SAS Multidimensional databases (MDDB), SAS files, and relational (RDBMS) databases into a single, powerful OLAP reporting environment. Support for HOLAP data groups is fully inte...

Dimension hierarchies represent a substantial part of the data warehouse model. Indeed they allow decision makers to examine data at different levels of detail with On-Line Analytical Processing (OLAP) operators such as drill-down and roll-up. The granularity levels which compose a dimension hierarchy are usually fixed during the design step of the data warehouse, according to the identified an...

Today's information systems mostly rely on relational databases for transactional data storage and manipulation. Throughout the years, massive amounts of data have been gathered. Those data, if properly analyzed, could serve as a basis for strategic decisions. For the purposes of analysis data is transformed from relational model (database) into dimensional model and stored in a data warehouse....

In this paper we discuss indexing methods for On-Line Analytical Processing (OLAP) databases. We start with a survey of existing indexing methods and discuss their advantages and shortcomings. We then propose extensions to conventional multidimensional indexing methods to make them more suitable for indexing OLAP data. We compare and contrast R-trees with bit-mapped indices which is the most po...

Due to unstructured and large amounts of data, relational databases are no longer suitable for data management. As a result, new known as NOSQL have been introduced. The issue is that such database difficult analyze. Online analytical processing (OLAP) the foundational technology analysis in business intelligence. Because these technologies were designed primarily systems, performing OLAP diffi...