Showing papers on "Data mart published in 2006"

Churn prediction and management system

Abstract: A system and method for managing churn among the customers of a business is provided. The system and method provide for an analysis of the causes of customer churn and identifies customers who are most likely to churn in the future. Identifying likely churners allows appropriate steps to be taken to prevent customers who are likely to churn from actually churning. The system included a dedicated data mart, a population architecture, a data manipulation module, a data mining tool and an end user access module for accessing results and preparing preconfigured reports. The method includes adopting an appropriate definition of churn, analyzing historical customer to identify significant trends and variables, preparing data for data mining, training a prediction model, verifying the results, deploying the model, defining retention targets, and identifying the most responsive targets.

Statistical modeling methods for determining customer distribution by churn probability within a customer population

Abstract: A system and method for managing churn among the customers of a business is provided. The system and method provide for an analysis of the causes of customer churn and identifies customers who are most likely to churn in the future. Identifying likely churners allows appropriate steps to be taken to prevent customers who are likely to chum from actually churning. The system included a dedicated data mart, a population architecture, a data manipulation module, a data mining tool and an end user access module for accessing results and preparing preconfigured reports. The method includes adopting an appropriate definition of churn, analyzing historical customer to identify significant trends and variables, preparing data for data mining, training a prediction model, verifying the results, deploying the model, defining retention targets, and identifying the most responsive targets.

Business intelligent architecture system and method

Abstract: The present disclosure describes a standardized logical model architecture, for use in building a business intelligent systems, and a system and method of using the logical model architecture to populate a data repository (e.g., a data mart/warehouse) with the data to satisfy reporting and data analysis needs.

Fraud protection using business process-based customer intent analysis

Abstract: Online fraud is reduced by identifying suspicious activities in real time and providing alerting so that interdiction may be performed. Historical customer behavior is used to identify and flag deviations in activity patterns. An HTTP data stream is parsed, intelligently filtered, and key data is extracted in real time. The key data is periodically extracted from network traffic and used to update corresponding summaries stored in a fraud data mart. The data mart is constantly incrementally updated so that the most current historical information is available to a rules engine for real time comparison with new customer data and patterns occurring on the network. Fraud-related business signatures are applied to this data stream and/or a data mart to identify suspicious online transactions. By understanding the customer session, the customer's intended use of the online application is derived and possible fraudulent activities identified.

A Requirement-driven Approach for Designing Data Warehouses

Abstract: . A data warehouse (DW) is an integrated and historised collection of data generally used to make strategic decisions by means of online analytical processing techniques. Most of the existing DW evelopment tools used nowadays in the industry focuses on the structures for data storage, e.g. applying the star or snowflake schema. We believe that DW that better suit the needs of decision makers would be delivered by concentrating more on their requirements. So far, very few approaches have been proposed to elicit DW requirements. This paper proposes a method, called CADWA, for guiding DW analyst in the elicitation of decisions-makers requirements and in their operationalization into a DW model. CADWA shifts the focus from where information from on how they should be structured and why they are needed. To comply with current practice, the approach starts with the elicitation of highlevel requirements, reuses a set of data mart (DM) models and produces a model for the new DW. The paper presents each stage of the CADWA approach, and provides illustrations with an example inspired from a real case.

System and method for integrated data mart defining start schema model processing business contact data from multiple contact channels

Abstract: Disclosed is a data collection and analysis system that is capable of extracting data from various disparate sources, i.e., contact channels, storing the data and analyzing the data to show trends in the business operation. The data is stored in a data model that uses a star schema approach to providing a unified data source. Analyzed data can be made available to users on a nearly real time basis that allows the users to view trends in business operation and plan accordingly.

Optimistic Coarse-Grained Cache Semantics for Data Marts

Abstract: Data marts and caching are two closely related concepts in the domain of multi-dimensional data. Both store pre-computed data to provide fast response times for complex OLAP queries, and for both it must be guaranteed that every query can be completely processed. However, they differ extremely in their update behaviour which we utilise to build a specific data mart extended by cache semantics. In this paper, we introduce a novel cache exploitation concept for data marts - coarse-grained caching - in which the containedness check for a multi-dimensional query is done through the comparison of the expected and the actual cardinalities. Therefore, we subdivide the multi-dimensional data into coarse partitions, the so called cubletets, which allow to specify the completeness criteria for incoming queries. We show that during query processing, the completeness check is done with no additional costs

Network Intrusion Detection System with Data Mart.

Abstract: Network Intrusion Detection Systems (NIDS) capture large amounts of data that is difficult or impractical to report and analyze directly from the capture device. It is also common to have more than one NIDS device and reporting from a consolidated multi-NIDS device. To provide a platform for multi-NIDS device reporting and analysis, this paper describes a consolidated database, or Data Mart design and implementation to store data from multiple Snort NIDS devices. This consolidated Snort Mart is designed for reporting and analysis and can provide a platform for better understanding of NIDS device information

Research and application of business intelligence in modern enterprise

Abstract: Aiming at the problems of modern enterprise information system,Business Intelligence architecture is introduced,a kind of improved system structure of business intelligence is proposed,a comprehensive method of business intelligence system construction is put forward.The essential technology of the business intelligence system is researched and realized including subject analysis choice,ETCL's process realization and data warehouse query optimization.The result in the practical application indicates that the using efficiency of the existed information data is heightened,and the ability of the enterprise decision analysis is improved.

The research of an intelligent object-oriented prototype for data warehouse

Abstract: In this paper, we intend to do research and implementation of an intelligent object-oriented prototype for data warehouse. We design an intelligent prototype with object-oriented methodology, also we summarize some basic requirements and data model constructing for applying data warehouse in population fields. Finally, we introduce the research of an agent-based algorithm to process the special information in data mining on data warehousing, together with the corresponding rule for mathematic model. It is fitful to be used especially on statistic field.

Application of Bussiness Intelligence in Modern Enterprise

Abstract: Aiming at the problems of modern enterprise information system, this paper introduces business intelligence architecture, proposes one kind of improvement system structure of business intelligence, puts forward a comprehensive method of business intelligence system construction, researches and realizes the essential technology of the business intelligence system:subject analysis choice,ETCL’s process realization and data warehouse query optimization. The result in the practical application indicates that the using efficiency of the existed information data has been heightened, and the ability of the enterprise decision analysis has been improved.

Using a multidimensional Data Model to Build a Prospecting Data Mart of Oil and Gas

Abstract: This paper firstly introduces the conception and building process of data mart,and then using analysis example of rock property in petroliferous basin and based on various software tools discusses the method and process of realizing data mart.Lastly the instance of prospecting target analysis in Erlian basin Saihantala depression using data mart is presented.The practice result indicates that in constructing petroleum prospecting information system data mart is more concise and more practical than data warehouse,and its generalization and application are more feasible.

Research on DSM Based Distribution Marketing Decision Support System

Abstract: DSM will be a routine job for the power company, however the exiting power marketing system can only do common transaction processing, and is lack of analysis on demand side management (DSM). So, this paper proposes to build a DSM oriented decision support system, first put forward a thought of building data warehouse and data mart based on the exiting transaction system, then design DSM subject for decision making, at the end of the paper, give an example to explain how to realize the subject through OLTP and data mining. This scheme can be used in the establishment of the distribution marketing decision support system for the power supply enterprises. Moreover it conforms to the request of constructing economy society.

Real time data warehousing and on line analytical processing at aberdeen test center

Abstract: This paper is a follow on to a paper presented at the 2005 International Telemetry Conference by Dr. Samuel Harley et. al., titled Data, Information, and Knowledge Management. This paper will describe new techniques and provide further detail into the inner workings of the VISION (Versatile Information System – Integrated, Online) Engineering Performance Data Mart.

MULTIDIMENSIONAL SCHEMA EVOLUTION - Integrating New OLAP Requirements

Abstract: Multidimensional databases are an effective support for OLAP processes. They improve the enterprise decision-making. These databases evolve with the decision maker requirements evolution and, are sensitive to data source changes. In this paper, we are interested in the evolution of the data mart schema due to the raise of new OLAP needs. Our approach determines first, what functional data marts will be able to cover a new requirement, if any, and secondly, decides on a strategy of integration. This leads either to the alteration of an existing data mart schema or, to the creation of a new schema suitable for the new requirement.

A web based Data Mart system for patient care

Abstract: A Project report submitted to School of Graduate Studies in partial fulfillment for the award of the Degree of Master of Science in Computer Science of Makerere University.

A gui for data mart schema alteration

Abstract: This paper is interested in the graphical manipulation of data mart schemes described in XML and issued from a generation module of multidimensional models. This manipulation is performed through a set of operations we have defined. These operations consist in adding, deleting and renaming the multidimensional

Shrinked Data Marts Enabled for Negative Caching

Abstract: Data marts storing pre-aggregated data, prepared for further roll-ups, play an essential role in data warehouse environments and lead to significant performance gains in the query evaluation. However, in order to ensure the completeness of query results on the data mart without to access the underlying data warehouse, null values need to be stored explicitly; this process is denoted as negative caching. Such null values typically occur in multidimensional data sets, which are naturally very sparse. To our knowledge, there is no work on shrinking the null tuples in a multi-dimensional data set within ROLAP. For these tuples, we propose a lossless compression technique, leading to a dramatic reduction in size of the data mart. Queries depending on null value information can be answered with 100% precision by partially inflating the shrunken data mart. We complement our analytical approach with an experimental evaluation using real and synthetic data sets, and demonstrate our results.