Andrea Romei

Preprocessing and Mining Web Log Data for Web Personalization

We describe the web usage mining activities of an on-going project, called ClickWorld, that aims at extracting models of the navigational behaviour of a web site users. The models are inferred from the access logs of a web server by means of data and web mining techniques. The extracted knowledge is deployed to the purpose of offering a personalized and proactive view of the web services to users. We first describe the preprocessing steps on access logs necessary to clean, select and prepare data for knowledge extraction. Then we show two sets of experiments: the first one tries to predict the sex of a user based on the visited web pages, and the second one tries to predict whether a user might be interested in visiting a section of the site.

A multidisciplinary survey on discrimination analysis

Most of the decisions in the todays knowledge society are taken on the basis of historical data by extracting models, patterns, profiles, and rules of human behavior in support of (automated) decision making. There is then the need of developing models, methods and technologies for modelling the processes of discrimination analysis in order to discover and prevent discrimination phenomena. In this respect, discrimination analysis from data should build over the large body of existing legal and economic studies. This paper intends to provide a multi-disciplinary survey of the literature on discrimination data analysis, including methods for data collection, empirical studies, controlled experiments, statistical evidence, and their legal requirements and grounds. We cover the following mainstream research lines: labour economic models, (quasi-)experimental approaches such as auditing and controlled experiments, profiling-based approaches such as racial profiling and credit markets, and the recently blooming research on knowledge discovery approaches.

Discrimination discovery in scientific project evaluation: A case study

Discovering contexts of unfair decisions in a dataset of historical decision records is a non-trivial problem. It requires the design of ad hoc methods and techniques of analysis, which have to comply with existing laws and with legal argumentations. While some data mining techniques have been adapted to the purpose, the state-of-the-art of research still needs both methodological refinements, the consolidation of a Knowledge Discovery in Databases (KDD) process, and, most of all, experimentation with real data. This paper contributes by presenting a case study on gender discrimination in a dataset of scientific research proposals, and by distilling from the case study a general discrimination discovery process. Gender bias in scientific research is a challenging problem, that has been tackled in the social sciences literature by means of statistical regression. However, this approach is limited to test an hypothesis of discrimination over the whole dataset under analysis. Our methodology couples data mining, for unveiling previously unknown contexts of possible discrimination, with statistical regression, for testing the significance of such contexts, thus obtaining the best of the two worlds.

Inductive database languages: requirements and examples

Inductive databases (IDBs) represent a database perspective on Knowledge discovery in databases (KDD). In an IDB, the KDD application can express both queries capable of accessing and manipulating data, and queries capable of generating, manipulating, and applying patterns allowing to formalize the notion of mining process. The feature that makes them different from other data mining applications is exactly the idea of looking at the support for knowledge discovery as an extension of the query process. This paper draws a list of desirable properties to be taken into account in the definition of an IDB framework. They involve several dimensions, such as the expressiveness of the language in representing data and models, the closure principle, the capability to provide a support for an efficient algorithm programming. These requirements are a basis for a comparative study that highlights strengths and weaknesses of existing IDB approaches. The paper focuses on the SQL-based ATLaS language/system, on the logic-based

Discovering Gender Discrimination in Project Funding

{\mathcal{LDL}++}

Survey on using constraints in data mining

language/system, and on the XML-based KDDML language/system.

A Case Study in Sequential Pattern Mining for IT-Operational Risk

Discrimination data analysis has been investigated for the last fifty years in a large body of social, legal, and economic studies. Recently, discrimination discovery and prevention has become a blooming research topic in the knowledge discovery community. This chapter provides a multi-disciplinary annotated bibliography of the literature on discrimination data analysis, with the intended objective to provide a common basis to researchers from a multi-disciplinary perspective.We cover legal, sociological, economic and computer science references.

Extending KDDML with a Visual Metaphor for the KDD Process

The selection of projects for funding can hide discriminatory decisions. We present a case study investigating gender discrimination in a dataset of scientific research proposals submitted to an Italian national call. The method for the analysis relies on a data mining classification strategy that is inspired by a legal methodology for proving evidence of social discrimination against protected-by-law groups.