Using Markov Chains for link prediction in adaptive web sites

The large number of Web pages on many Web sites has raised navigational problems. Markov chains have recently been used to model user navigational behavior on the World Wide Web (WWW). In this paper, we propose a method for constructing a Markov model of a Web site based on past visitor behavior. We use the Markov model to make link predictions that assist new users to navigate the Web site. An algorithm for transition probability matrix compression has been used to cluster Web pages with similar transition behaviors and compress the transition matrix to an optimal size for efficient probability calculation in link prediction. A maximal forward path method is used to further improve the efficiency of link prediction. Link prediction has been implemented in an online system called ONE (Online Navigation Explorer) to assist users' navigation in the adaptive Web site

A fine grained heuristic to capture web navigation patterns

In previous work we have proposed a statistical model to capture the user behaviour when browsing the web. The user navigation information obtained from web logs is modelled as a hypertext probabilistic grammar (HPG) which is within the class of regular probabilistic grammars. The set of highest probability strings generated by the grammar corresponds to the user preferred navigation trails. We have previously conducted experiments with a Breadth-First Search algorithm (BFS) to perform the exhaustive computation of all the strings with probability above a specified cut-point, which we call the rules. Although the algorithm’s running time varies linearly with the number of grammar states, it has the drawbacks of returning a large number of rules when the cut-point is small and a small set of very short rules when the cut-point is high. In this work, we present a new heuristic that implements an iterative deepening search wherein the set of rules is incrementally augmented by first exploring trails with high probability. A stopping parameter is provided which measures the distance between the current rule-set and its corresponding maximal set obtained by the BFS algorithm. When the stopping parameter takes the value zero the heuristic corresponds to the BFS algorithm and as the parameter takes values closer to one the number of rules obtained decreases accordingly. Experiments were conducted with both real and synthetic data and the results show that for a given cut-point the number of rules induced increases smoothly with the decrease of the stopping criterion. Therefore, by setting the value of the stopping criterion the analyst can determine the number and quality of rules to be induced; the quality of a rule is measured by both its length and probability

Community dynamics mining

In this paper we propose a model to analyze community dynamics. Recently, several methods and tools have been proposed to extract communities from static graphs. However, since communities are not static, but change over time, it is necessary to provide methods to determine and observe the community transitions and to extract the factors that cause the development. We regard a community as an object that exists over time and propose to observe community transitions along the time axis. For this we partition the time axis under observation by time windows. In each time window, a set of interactions between community participants is aggregated. These static networks are analyzed for subcommunities by applying community detection mechanisms. Through this we detect communities in each interval and can observe if communities persist over time or undergo a transition. We present community transitions and the observable indicators for the respective development. We furthermore present a software environment that incorporates several community detection and analysis methods to analyze community transitions. It supports a dynamic temporal community analysis and provides several forms of visualizations and analysis settings thus providing an interactive tool to observe community dynamics

Reference Process Flows for Telecommunication Companies - An Extension of the eTOM Model

The telecommunication market is experiencing substantial changes. New business models, innovative services, and technologies require reengineering, transformation, and process standardization. Enterprise Architecture Frameworks support the transformation by specifying methods, procedures, and reference models. With the Enhanced Telecom Operation Map (eTOM), the TM Forum offers an international de facto reference process framework, based on specific features and requirements of the telecommunication industry. However, this reference framework only offers a hierarchical collection of processes on different levels of abstraction; a control view in terms of a sequential ordering of tasks and hence a real process flow as well as an end-to-end view on the customer are missing. In this paper, we extend the eTOM reference model by reference process flows, in which we abstract and generalize the knowledge about processes in telecommunication companies. With reference process flows, we aim to assist companies in achieving a structured and transparent re-structuring and re-design of their processes. We demonstrate the applicability and usefulness of our reference process flows in two case studies, and evaluate them by means of criteria for reference model evaluation. Our reference process flows have been accepted as a standard by the TM Forum and published as part of eTOM version 9. We further elaborate on those components of our approach which can be applied outside the telecommunication industry

Generating dynamic higher-order Markov models in web usage mining

Markov models have been widely used for modelling users’ web navigation behaviour. In previous work we have presented a dynamic clustering-based Markov model that accurately represents second-order transition probabilities given by a collection of navigation sessions. Herein, we propose a generalisation of the method that takes into account higher-order conditional probabilities. The method makes use of the state cloning concept together with a clustering technique to separate the navigation paths that reveal differences in the conditional probabilities. We report on experiments conducted with three real world data sets. The results show that some pages require a long history to understand the users choice of link, while others require only a short history. We also show that the number of additional states induced by the method can be controlled through a probability threshold parameter

Experimental Investigation of the Effects of Different Market Mechanisms for Electronic Knowledge Markets

Knowledge sharing can be seen as a social dilemma. Mutual knowledge sharing can lead to a benefit for all the participants. However, establishing voluntary knowledge sharing can be difficult because each member benefits from the knowledge offered by others but gains little from the own contribution. Knowledge markets could overcome this problem. To analyze knowledge markets we designed and implemented the Data Trader Game. This computer-assisted game was used for the real-life experiments of different market mechanisms and their effects on knowledge sharing

Parallel Evaluation of Multi-join Queries

A number of execution strategies for parallel evaluation of multi-join queries have been proposed in the literature. In this paper we give a comparative performance evaluation of four execution strategies by implementing all of them on the same parallel database system, PRISMA/DB. Experiments have been done up to 80 processors. These strategies, coming from the literature, are named: Sequential Parallel, Synchronous Execution, Segmented Right-Deep, and Full Parallel. Based on the experiments clear guidelines are given when to use which strategy. This is an extended abstract; the full paper appeared in Proc. ACM SIGMOD'94, Minneapolis, Minnesota, May 24–27, 199