Usage tracking by public information system mediator

Abstract

Keeping track of users' activity is a common scenario in nowadays Internet. Large volumes of usage data are collected and stored on Web servers, proxies and clients throughout the network on daily basis. Properly interpreted, these data are a valuable resource for determining effective marketing strategies, automatic generation of personalized user profiles, and network performance optimization. Information and data correlation needed to perform these tasks are extracted from collected data through data mining process.This paper presents usage tracking performed by the public information system mediator'. Mediator provides a designtime support for development of distributed services and a run-time support for their execution. One of the aspects of the service run-time support is collecting service's usage. Usage collecting mechanism introduced by the mediator is adaptable through service specific filters. Collected usage data are grouped into general and service specific usage data. Based on these two groups of usage data, two types of data mining processes are being proposed. General data mining uses general usage data to extract the usage patterns and optimize performance of the mediator. Service specific data mining is done by the service vendor through semantic interpretation of service specific usage data and is used for extending service offerings and optimizing service performance.

Usage Tracking Components for Service-Oriented Middleware Systems

“…Dynasoar [9] and MidArc [10] monitoring frameworks include e-business usage tracking middleware components based on SOAP-intermediaries [11]. They are both designed for service-oriented systems where they provide facilities for SOAP message interception, filtering, and data extraction.…”

“…In order to integrate them with the host system, GUT and LUT services are registered with SDM service available on their host nodes (6)(7)(8). In addition, LUT services also register with GUT service as sources of aggregated usage tracking records (9)(10).…”