Data Mining in Electronic Commerce

Modern business is rushing toward e-commerce. If the transition is done properly, it enables better management, new services, lower transaction costs and better customer relations. Success depends on skilled information technologists, among whom are statisticians. This paper focuses on some of the contributions that statisticians are making to help change the business world, especially through the development and application of data mining methods. This is a very large area, and the topics we cover are chosen to avoid overlap with other papers in this special issue, as well as to respect the limitations of our expertise. Inevitably, electronic commerce has raised and is raising fresh research problems in a very wide range of statistical areas, and we try to emphasize those challenges.Comment: Published at http://dx.doi.org/10.1214/088342306000000204 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Преглед на технологични решения за управление на знания

The present paper focuses on a new managerial discipline emerging in the last few years of the 20th century. At the beginning are introduced some basic concepts used in the theory and practice of Knowledge Management, and are presented the benefits for utilization of Knowledge management. The main emphasis of the paper is on the technological solutions applied in the organizations at different stages of the knowledge management life cycle, whereas a summary is made of the types of technologies described in the theory and practice. Finally, are presented survey data on the real application of various knowledge management technologies in the organization

REVIEW OF TECHNOLOGY SOLUTIONS FOR KNOWLEDGE MANAGEMENTi

The present paper focuses on Knowledge Management (KM) as a new managerial discipline emerging in the last few years of the 20th century. The main emphasis of the paper is on the technological solutions applied in the organizations at different stages of the KM life cycle. It makes a classification of the types of technologies described in the theory and practice based on the main KM processes. Finally, are presented survey data on the real application of various knowledge management technologies in the organizations

Decision Support System and Customer Relationship Management as Components of the Cybernetic System Enterprise

This study analyzes the role played by the information system and its component, the software system, in a larger system - the Enterprise. In this context, the paper focuses on the structure of Decision Support System and Customer Relationship Management and their benefits in the functioning of the global system, by examining the conditions of implementation of these tools in the organization. We will show that used independently these tools offer reduced services, but when interconnected, they become a very powerful tool for command and control. Viability, evolution and autonomy requested by users for their information system are obtained more easily by a systemic-cybernetic approach to the Enterprise.DSS, Data Warehouse, CRM, Information System, Cybernetic System

Pay One, Get Hundreds for Free: Reducing Cloud Costs through Shared Query Execution

Cloud-based data analysis is nowadays common practice because of the lower system management overhead as well as the pay-as-you-go pricing model. The pricing model, however, is not always suitable for query processing as heavy use results in high costs. For example, in query-as-a-service systems, where users are charged per processed byte, collections of queries accessing the same data frequently can become expensive. The problem is compounded by the limited options for the user to optimize query execution when using declarative interfaces such as SQL. In this paper, we show how, without modifying existing systems and without the involvement of the cloud provider, it is possible to significantly reduce the overhead, and hence the cost, of query-as-a-service systems. Our approach is based on query rewriting so that multiple concurrent queries are combined into a single query. Our experiments show the aggregated amount of work done by the shared execution is smaller than in a query-at-a-time approach. Since queries are charged per byte processed, the cost of executing a group of queries is often the same as executing a single one of them. As an example, we demonstrate how the shared execution of the TPC-H benchmark is up to 100x and 16x cheaper in Amazon Athena and Google BigQuery than using a query-at-a-time approach while achieving a higher throughput

Quality measures for ETL processes: from goals to implementation

Extraction transformation loading (ETL) processes play an increasingly important role for the support of modern business operations. These business processes are centred around artifacts with high variability and diverse lifecycles, which correspond to key business entities. The apparent complexity of these activities has been examined through the prism of business process management, mainly focusing on functional requirements and performance optimization. However, the quality dimension has not yet been thoroughly investigated, and there is a need for a more human-centric approach to bring them closer to business-users requirements. In this paper, we take a first step towards this direction by defining a sound model for ETL process quality characteristics and quantitative measures for each characteristic, based on existing literature. Our model shows dependencies among quality characteristics and can provide the basis for subsequent analysis using goal modeling techniques. We showcase the use of goal modeling for ETL process design through a use case, where we employ the use of a goal model that includes quantitative components (i.e., indicators) for evaluation and analysis of alternative design decisions.Peer ReviewedPostprint (author's final draft