Showing papers on "Online analytical processing published in 2004"

System and method for real-time, personalized, dynamic, interactive voice services for information related to existing travel schedule

Abstract: A system and method for creation and automatic deployment of personalized, dynamic and interactive voice services relating to subscriber travel, including information derived from on-line analytical processing (OLAP) systems. More specifically, the invention relates to a system and method that enable personalized delivery of travel-related information in real-time, via natural language voice communication with a voice-enabled terminal device.

XQuery on SQL hosts

Abstract: Relational database systems may be turned into efficient XML and XPath processors if the system is provided with a suitable relational tree encoding. This paper extends this relational XML processing stack and shows that an RDBMS can also serve as a highly efficient XQuery runtime environment. Our approach is purely relational: XQuery expressions are compiled into SQL code which operates on the tree encoding. The core of the compilation procedure trades XQuery's notions of variable scopes and nested iteration (FLWOR blocks) for equi-joins. The resulting relational XQuery processor closely adheres to the language semantics, e.g., it obeys node identity as well as document and sequence order, and can support XQuery's full axis feature. The system exhibits quite promising performance figures in experiments. Somewhat unexpectedly, we will also see that the XQuery compiler can make good use of SQL's OLAP functionality.

Computer-implemented system and method for visualizing OLAP and multidimensional data in a calendar format

Abstract: Computer-implemented systems and methods for displaying multidimensional data as graphical time-based objects. A system and method could include associating actual calendar units with time dimension members. The association can be based on the time periods and the corresponding time-level information in the cube's time dimension hierarchies. Query results involving time periods and time period analysis are displayed as graph chart objects within the time period's visual presentation.

High-dimensional OLAP: a minimal cubing approach

Abstract: Data cube has been playing an essential role in fast OLAP (online analytical processing) in many multi-dimensional data warehouses. However, there exist data sets in applications like bioinformatics, statistics, and text processing that are characterized by high dimensionality, e.g., over 100 dimensions, and moderate size, e.g., around 106 tuples. No feasible data cube can be constructed with such data sets. In this paper we will address the problem of developing an efficient algorithm to perform OLAP on such data sets. Experience tells us that although data analysis tasks may involve a high dimensional space, most OLAP operations are performed only on a small number of dimensions at a time. Based on this observation, we propose a novel method that computes a thin layer of the data cube together with associated value-list indices. This layer, while being manageable in size, will be capable of supporting flexible and fast OLAP operations in the original high dimensional space. Through experiments we will show that the method has I/O costs that scale nicely with dimensionality. Furthermore, the costs are comparable to that of accessing an existing data cube when full materialization is possible.

Skyline Cardinality for Relational Processing

Abstract: The skyline clause—also called the Pareto clause—recently has been proposed as an extension to SQL. It selects the tuples that are Pareto optimal with respect to a set of designated skyline attributes. This is the maximal vector problem in a relational context, but it represents a powerful extension to SQL which allows for the natural expression of on-line analytic processing (OLAP) queries and preferences in queries.

Towards Context-Aware Data Management for Ambient Intelligence

Abstract: Ambient Intelligence (AmI) is a vision of future Information Society, where people are surrounded by an electronic environment which is sensitive to their needs, personalized to their requirements, anticipatory of their behavior, and responsive to their presence. It emphasizes on greater user-friendliness, user-empowerment, and more effective service support, with an aim to make peoplersquos daily activities more convenient, thus improving the quality of human life. To make AmI real, effective data management support is indispensable. High-quality information must be available to any user, anytime, anywhere, and on any lightweight device. Beyond that, AmI also raises many new challenges related to context-awareness and natural user interaction, entailing us to re-think current database techniques. The aim of this paper is to address the impact of AmI, particularly its user-centric context-awareness requirement on data management strategies and solutions. We first provide a multidimensional view of database access context. Taking diverse contextual information into account, we then present five context-aware data management strategies, using the most fundamental database operation-context-aware query request as a case in point. We execute the proposed strategies via a two-layered infrastructure, consisting of public data manager(s) and a private data manager. Detailed steps of processing a context-aware query are also described in the paper.

Method of and apparatus for data aggregation utilizing a multidimensional database and multi-stage data aggregation operations

Abstract: An improved method of and apparatus for aggregating data including a scalable multi-dimensional database (MDDB) storing multidimensional data logically organized along N dimensions and a high performance aggregation engine that performs multi-stage data aggregation operations on the multidimensional data. A first stage of such data aggregation operations is performed along a first dimension of the N dimensions; and a second stage of such data aggregation operations is performed for a given slice in the first dimension along another dimension of the N dimensions. Such multi-stage data aggregation operations achieve a significant increase in system performance (e.g. deceased access/search time). The MDDB and high performance aggregation engine of the present invention may be integrated into a standalone data aggregation server supporting an OLAP system (one or more OLAP servers and clients), or may be integrated into a database management system (DBMS), thus achieving improved user flexibility and ease of use. The improved DBMS system of the present invention can be used to realize an improved Data Warehouse for supporting on-line analytical processing (OLAP) operations or to realize an improved informational database system, operational database system, or the like.

Securing OLAP data cubes against privacy breaches

Abstract: An OLAP (On-line Analytic Processing) system with insufficient security countermeasures may disclose sensitive information and breach an individual's privacy. Both unauthorized accesses and malicious inferences may lead to such inappropriate disclosures. Existing access control models in relational databases are unsuitable for the multi-dimensional data cubes used by OLAP. Inference control methods in statistical databases are expensive and apply to limited situations only. We first devise a flexible framework for specifying authorization objects in data cubes. The framework can partition a data cube both vertically based on dimension hierarchies and horizontally based on slices of data. We then study how to control inferences in data cubes. The proposed method eliminates both unauthorized accesses and malicious inferences. Its effectiveness does not depend on specific types of aggregation functions, external knowledge, or sensitivity criteria. The technique is efficient and readily implementable. Its on-line performance overhead is comparable to that of the minimal security requirement. Its enforcement requires little modification to existing OLAP systems.

OLAP Hierarchies: A Conceptual Perspective

Abstract: OLAP (On-Line Analytical Processing) tools support the decision-making process by giving users the possibility to dynamically analyze high volumes of historical data using operations such as roll-up and drill-down. These operations need well-defined hierarchies in order to prepare automatic calculations. However, many kinds of complex hierarchies arising in real-world situations are not addressed by current OLAP implementations. Based on an analysis of real-world applications and scientific works related to multidimensional modeling, this paper presents a conceptual classification of hierarchies and proposes graphical notations for them based on the ER model. A conceptual representation of hierarchies allows the designer to properly represent users’ requirements in multidimensional modeling and offers a common vision of these hierarchies for conceptual modeling and OLAP tools implementers.

A Genetic Selection Algorithm for OLAP Data Cubes

Abstract: Multidimensional data analysis, as supported by OLAP (online analytical processing) systems, requires the computation of many aggregate functions over a large volume of historically collected data. To decrease the query time and to provide various viewpoints for the analysts, these data are usually organized as a multidimensional data model, called data cubes. Each cell in a data cube corresponds to a unique set of values for the different dimensions and contains the metric of interest. The data cube selection problem is, given the set of user queries and a storage space constraint, to select a set of materialized cubes from the data cubes to minimize the query cost and/or the maintenance cost. This problem is known to be an NP-hard problem. In this study, we examined the application of genetic algorithms to the cube selection problem. We proposed a greedy-repaired genetic algorithm, called the genetic greedy method. According to our experiments, the solution obtained by our genetic greedy method is superior to that found using the traditional greedy method. That is, within the same storage constraint, the solution can greatly reduce the amount of query cost as well as the cube maintenance cost.

A new OLAP aggregation based on the AHC technique

Abstract: Nowadays, decision support systems are evolving in order to handle complex data. Some recent works have shown the interest of combining on-line analysis processing (OLAP) and data mining. We think that coupling OLAP and data mining would provide excellent solutions to treat complex data. To do that, we propose an enhanced OLAP operator based on the agglomerative hierarchical clustering (AHC). The here proposed operator, called OpAC (Operator for Aggregation by Clustering) is able to provide significant aggregates of facts refereed to complex objects. We complete this operator with a tool allowing the user to evaluate the best partition from the AHC results corresponding to the most interesting aggregates of facts.

Nearest Neighbor Search:: A Database Perspective

Abstract: Modern applications are both data and computationally intensive and require the storage and manipulation of voluminous traditional (alphanumeric) and nontraditional data sets (images, text, geometric objects, time-series). Examples of such emerging application domains are: Geographical Information Systems (GIS), Multimedia Information Systems, CAD/CAM, Time-Series Analysis, Medical Information Sstems, On-Line Analytical Processing (OLAP), and Data Mining. These applications pose diverse requirements with respect to the information and the operations that need to be supported. From the database perspective, new techniques and tools therefore need to be developed towards increased processing efficiency. This monograph explores the way spatial database management systems aim at supporting queries that involve the space characteristics of the underlying data, and discusses query processing techniques for nearest neighbor queries. It provides both basic concepts and state-of-the-art results in spatial databases and parallel processing research, and studies numerous applications of nearest neighbor queries.

Discovering multidimensional structure in relational data

Abstract: On-Line Analytical Processing (OLAP) systems based on multidimensional databases are essential elements of decision support. However, most existing data is stored in ordinary relational OLTP databases, i.e., data has to be (re-) modeled as multidimensional cubes before the advantages of OLAP tools are available. In this paper we present an approach for the automatic construction of multidimensional OLAP database schemas from existing relational OLTP databases, enabling easy OLAP design and analysis for most existing data sources. This is achieved through a set of practical and effective algorithms for discovering multidimensional schemas from relational databases. The algorithms take a wide range of available metadata into account in the discovery process, including functional and inclusion dependencies, and key and cardinality information.

Method and apparatus for accessing multidimensional data

Abstract: A method of indexing data in a multidimensional database includes creating a multidimensional logical access model, creating a multidimensional data storage model in which data is located in cells that are stored and retrieved in blocks, gathering data access information derived from one or more user queries of the database, and reorganizing one or more selected cells in the multidimensional data storage model based on the data access information to reduce the time taken to access the one or more selected cells in response to a user query of the database. A computerized apparatus in communication with a multidimensional database includes a program to perform the method. A computer readable medium contains instructions to cause a computer to perform the method.

GeoDWFrame: A framework for guiding the design of geographical dimensional schemas

Abstract: Data Warehouse (DW) is a dimensional database for providing decision support by means of on-line analytical processing (OLAP) techniques. Another technology also used to provide decision support is Geographical Information System (GIS). Much research aims at integrating these technologies, but there are still some open questions, particularly regarding the design of a geographical dimensional data schema. This paper discusses some related work and proposes GeoDWFrame that is a framework based on the star schema and has been specified as guidance for designing geographical dimensional schemas. Some experimental results are also given.

GeoDWFrame: A Framework for Guiding the Design of Geographical Dimensional Schemas

Abstract: Data Warehouse (DW) is a dimensional database for providing decision support by means of on-line analytical processing (OLAP) techniques. Another technology also used to provide decision support is Geographical Information System (GIS). Much research aims at integrating these technologies, but there are still some open questions, particularly regarding the design of a geographical dimensional data schema. This paper discusses some related work and proposes GeoDWFrame that is a framework based on the star schema and has been specified as guidance for designing geographical dimensional schemas. Some experimental results are also given.

OLAP-based customer behavior profiling method and system

Abstract: An OLAP-based method and system for profiling customer behavior that can be utilized to detect telecommunication fraud. First, call records are received. Next, a calling profile cube (e.g., a multi-customer profile cube) is generated based on the call records. A volume-based calling pattern cube (e.g., a calling pattern cube for each individual customer) is then generated based on the multi-customer profile cube. The volume-based calling pattern cube is then compared with known fraudulent volume-based calling patterns. If the similarities generated by the comparison reaches or exceeds a predetermined threshold, then the particular caller with the calling pattern being analyzed is considered suspicious. In this manner, suspicious calling activity can be detected, and appropriate remedial actions, such as further investigation or the cancellation of telephone services, can be taken.

Discovering Multidimensional Structure in Relational Data

Abstract: On-Line Analytical Processing (OLAP) systems based on multidimensional databases are essential elements of decision support. However, most existing data is stored in “ordinary” relational OLTP databases, i.e., data has to be (re-) modeled as multidimensional cubes before the advantages of OLAP tools are available.

Exposing multidimensional calculations through structured query language

Abstract: Accessing results of calculations on multidimensional data using relational database statements, such as SQL commands, involves a relational database server communicating with a multidimensional database server to cause the multidimensional database server to extract, and to perform calculations on, the multidimensional data. In response to a relational database statement in a language supported by the relational server, which includes an expression of an operation on the multidimensional data in a language supported by the multidimensional server, the multidimensional server extracts the required multidimensional data, performs the requested operation (e.g., an OLAP DML operation) on the data according to the specified expression, and provides results of the multidimensional operation to the relational database server. In one embodiment, the relational server passes information to the multidimensional server that uniquely identifies the location of the particular multidimensional data in the n-dimensional objects.

Supporting dimension updates in an OLAP server

Abstract: Commercial on line analytical processing (OLAP) systems usually treat OLAP dimensions as static entities. In practice, dimension updates are often needed to adapt the warehouse to changing requirements. In earlier work, we defined a taxonomy for these dimension updates and a minimal set of operators to perform them. In this paper we present TSOLAP, an OLAP server supporting fully dynamic dimensions. TSOLAP conforms to the OLE DB for OLAP norm, so it can be used by any client application based on this norm, and can use as back-end any conformant relational server. We incorporate dimension update support to MDX, Microsoft's language for OLAP, and introduce TSShow, a visualization tool for dimensions and data cubes. Finally, we present the results of a real-life case study in the application of TSOLAP to a medium-sized medical center.

Systems, methods, and computer program products that automatically discover metadata objects and generate multidimensional models

Abstract: Systems, methods, and computer products that include an automated discovery process that discovers useful metadata objects from an intelligent starting point thereby generating at least one multidimensional model for OLAP analysis. Further, generation of the intelligent starting point may be derived by use of a multidimensional analysis program that analyzes the results of query mining and query analysis. The preferred embodiment of the present invention determines whether metadata useful for OLAP analysis exists by evaluating patterns found in the queries. In addition to using the starting point derived from the results of query mining and query analysis, the preferred embodiment of the present invention may also limit search parameters to narrow the scope of searching for an intelligent starting point and thereby both increase the probability of producing an accurate cube multidimensional model and increase the efficiency of determining the intelligent starting point.

Adaptive Virtual Partitioning for OLAP Query Processing in a Database Cluster

Abstract: OLAP queries are typically heavy-weight and ad-hoc thus requiring high storage capacity and processing power. In this paper, we address this problem using a database cluster which we see as a cost-effective alternative to a tightly-coupled multiprocessor. We propose a solution to efficient OLAP query processing using a simple data parallel processing technique called adaptive virtual partitioning which dynamically tunes partition sizes, without requiring any knowledge about the database and the DBMS. To validate our solution, we implemented a Java prototype on a 32 node cluster system and ran experiments with typical queries of the TPC-H benchmark. The results show that our solution yields linear, and sometimes superlinear, speedup. In many cases, it outperforms traditional virtual partitioning by factors superior to 10.

An analysis of additivity in OLAP systems

Abstract: Accurate summary data is of paramount concern in data warehouse systems; however, there have been few attempts to completely characterize the ability to summarize measures. The sum operator is the typical aggregate operator for summarizing the large amount of data in these systems. We look to uncover and characterize potentially inaccurate summaries resulting from aggregating measures using the sum operator. We discuss the effect of classification hierarchies, and non-, semi-, and fully- additive measures on summary data, and develop a taxonomy of the additive nature of measures. Additionally, averaging and rounding rules can add complexity to seemingly simple aggregations. To deal with these problems, we describe the importance of storing metadata that can be used to restrict potentially inaccurate aggregate queries. These summary constraints could be integrated into data warehouses, just as integrity constraints and are integrated into OLTP systems. We conclude by suggesting methods for identifying and dealing with non- and semi- additive attributes.

Architecture of sensor based agricultural information system for effective planning of farm activities

Abstract: The effectiveness of decision-making in agriculture domain can be improved by integrating current local environmental conditions with agricultural information system (AIS). To achieve this objective a series of functional blocks is proposed; which involves collection of environmental parameters from the farm, merging data from multiple sensors, communicating the collected data to the application server, extracting required information, defining ETL process, integration with ancillary data, analysis and presentation services. Web service based architecture comprising of distributed components is proposed to support the computational need. The proposed architecture extends analytical capabilities of end users with the help of multi dimensional expressions and OLAP.

Query consolidation for retrieving data from an OLAP cube

Abstract: Queries to obtain data from an OLAP cube are consolidated. Queries are consolidated to reduce the number of database hits to retrieve data from an OLAP cube. Instead of querying the OLAP cube for each cell in a free-form report, a consolidated query is used to obtain the desired information. The consolidated query may contain requests for data from different dimensions within the OLAP cube. The cells in the spreadsheet are parsed to determine the dimensions of the OLAP cube that are used within the spreadsheet cell. A list of dimensions accessed by the spreadsheet cells is compiled and the query is constructed by adding default dimensions to each cell as necessary to complete the query.

Analytical Synopses for Approximate Query Answering in OLAP Environments

Abstract: In this paper we present a technique based on an analytical interpretation of multi-dimensional data and on the well-known Least Squares Approximation (LSA) method for supporting approximate aggregate query answering in OLAP environments, the most common application interfaces for a Data Warehouse Server (DWS). Our technique consists in building data synopses by interpreting the original data distribution as a set of discrete functions. These synopses, called Δ-Syn, are obtained by approximating data with a set of polynomial coefficients, and storing these coefficients instead of the original data. Queries are issued on the compressed representation, thus reducing the number of disk accesses needed to evaluate the answer. We also provide some experimental results on several kinds of synthetic OLAP data cubes.

RAM: a multidimensional array DBMS

Abstract: Application areas beyond (simple) administrative tasks are dominated by custom built solutions Tasks like multimedia analysis require a view on data different from that offered by most database management systems: the set based data model may no longer suffice. To address this issue we introduce RAM: a multidimensional array database system The concept of an array database system is not new, however our approach differs from earlier work in that we realize this new view on data by mapping it onto a traditional relational schema This approach unites the strengths of existing database systems with the added benefits of bulk array processing.

System and method for cross attribute analysis and manipulation in online analytical processing (olap) and multi-dimensional planning applications by dimension splitting

Abstract: A system (400) for cross attribute analysis for sales data in a multi-dimensional planning system. The system (400) includes a set of processing modules (416) that performs cross attribute analysis and manipulation in online analytical processing (OLAP) and multi-dimensional planning applications (436) dimension splitting. A number of processing module are utilized to perform the required processing. The system (400) includes a hierarchy processing module for aggregating data up a hierarchical data structure, a dimension splitting module for creating pseudo-hierarchical data structures from data within the hierarchical data structure, and a multi-dimensional data viewing module for displaying a set of multi-dimensional data set according to the hierarchical data structure in a multi-dimensional spreadsheet. A single dimension corresponds to an attribute of the data contained within the hierarchical data structure.

Method for dynamic profiling

Abstract: According to the invention, techniques for profiling of human behavior based upon analyzing data contained in databases, data marts and data warehouses. In an exemplary embodiment, the invention provides for creating a dynamic customer profile by analyzing relationships in data from one or more data sources of an enterprise. The method can be used with many popular visualization tools, such as On Line Analytical Processing (OLAP) tools and the like. The method is especially useful in conjunction with a meta-model based technique for modeling the enterprise data. The enterprise is typically a business activity, but can also be other loci of human activity. The human behavior profiled is typically that of a customer, but can be any other type of human behavior. Embodiments according to the invention can display data from a variety of sources in order to provide visual representations of data in a data warehousing environment.