Alessandro Bozzon

An Introduction to Information Retrieval

Information retrieval is a discipline that deals with the representation, storage, organization, and access to information items. The goal of information retrieval is to obtain information that might be useful or relevant to the user: library card cabinets are a “traditional” information retrieval system, and, in some sense, even searching for a visiting card in your pocket to find out a colleague’s contact details might be considered as an information retrieval task. In this chapter we introduce information retrieval as a scientific discipline, providing a formal characterization centered on the notion of relevance. We touch on some of its challenges and classic applications and then dedicate a section to its main evaluation criteria: precision and recall.

Conceptual modeling and code generation for rich internet applications

This paper addresses conceptual modeling and automaticcode generation for Rich Internet Applications, a variant ofWeb-based systems bridging desktop and thin-client Webinterfaces. We show how classical Web modeling conceptsare not enough to capture the specificity of RIAs, extend anexisting Web modeling language, and provide an implementationof a CASE tool for visual modeling and code generationfrom RIA-aware specifications. Experimentation of theproposed approach in real-world scenarios is also reported.

Choosing the right crowd: expert finding in social networks

Expert selection is an important aspect of many Web applications, e.g., when they aim at matching contents, tasks or advertisement based on user profiles, possibly retrieved from social networks. This paper focuses on selecting experts within the population of social networks, according to the information about the social activities of their users. We consider the following problem: given an expertise need (expressed for instance as a natural language query) and a set of social network members, who are the most knowledgeable people for addressing that need? We considers social networks both as a source of expertise information and as a route to reach expert users, and define models and methods for evaluating peoples expertise by considering their profiles and by tracing their activities in social networks. For matching queries to social resources, we use both text analysis and semantic annotation. An extensive set of experiments shows that the analysis of social activities, social relationships, and socially shared contents helps improving the effectiveness of an expert finding system.

Liquid query: multi-domain exploratory search on the web

In this paper we propose the Liquid Query paradigm, to support users in finding responses to multi-domain queries through exploratory information seeking across structured information sources (Web documents, deep Web data, and personal data repositories), wrapped by means of a uniform notion of search service. Liquid Query aims at filling the gap between general-purpose search engines, which are unable to find information spanning multiple topics, and domain-specific search systems, which cannot go beyond their domain limits. The Liquid Query interface consists of interaction primitives that let users pose questions and explore results spanning over multiple sources incrementally, thus getting closer and closer to the sought information. We demonstrate our approach with a prototype built upon the YQL (Yahoo! Query Language) framework.

Engineering rich internet applications with a model-driven approach

Rich Internet Applications (RIAs) have introduced powerful novel functionalities into the Web architecture, borrowed from client-server and desktop applications. The resulting platforms allow designers to improve the users experience, by exploiting client-side data and computation, bidirectional client-server communication, synchronous and asynchronous events, and rich interface widgets. However, the rapid evolution of RIA technologies challenges the Model-Driven Development methodologies that have been successfully applied in the past decade to traditional Web solutions. This paper illustrates an evolutionary approach for incorporating a wealth of RIA features into an existing Web engineering methodology and notation. The experience demonstrates that it is possible to model RIA application requirements at a high-level using a platform-independent notation, and generate the client-side and server-side code automatically. The resulting approach is evaluated in terms of expressive power, ease of use, and implementability.

Sparrows and Owls: Characterisation of Expert Behaviour in StackOverflow

Question Answering platforms are becoming an important repository of crowd-generated knowledge. In these systems a relatively small subset of users is responsible for the majority of the contributions, and ultimately, for the success of the Q/A system itself. However, due to built-in incentivization mechanisms, standard expert identification methods often misclassify very active users for knowledgable ones, and misjudge activeness for expertise. This paper contributes a novel metric for expert identification, which provides a better characterisation of users’ expertise by focusing on the quality of their contributions. We identify two classes of relevant users, namely sparrows and owls, and we describe several behavioural properties in the context of the StackOverflow Q/A system. Our results contribute new insights to the study of expert behaviour in Q/A platforms, that are relevant to a variety of contexts and applications.

A Conceptual Modeling Approach to Business Service Mashup Development

Professional mashups that include complex choreographies, data mediation, and result publishing within Web pages are still affected by implementation and design practices that rely either on very simple models or on low-level scripting and programming skills of developers, thus hampering the use of mashups in business context as rapid solution to immediate problems. Indeed, industrialization of their development is still a hard objective to achieve.We propose a design methodology based on visual models to improve the quality and the productivity of service mashups and presentation of the results, thus increasing their acceptance as professional applications in the business scenario. Existing software engineering methods are combined together in an innovative mix, comprising standard business process modeling languages (namely, BPMN) to describe a high-level view of the mashup orchestration and on WebML (Web Modeling Language) to specify the detailed Web application model, including Web service interactions, hypertext navigation, event management, and rich user interfaces.

Modeling distributed events in data-intensive rich internet applications

Rich Internet applications (RIAs) enable novel usage scenarios by overcoming the traditional paradigms of Web interaction. Conventional Web applications can be seen as reactive systems in which events are 1) produced by the user acting upon the browser HTML interface, and 2) processed by the server hosting the application state and logic. In RIAs, distribution of data and computation across client and server broadens the classes and features of the produced events as they can originate, be detected, notified, and processed in a variety of ways. In this work, we investigate how events can be explicitly described and coupled to the other concepts of a Web modeling language in order to specify collaborative Rich Internet applications.

Web Information Retrieval

With the proliferation of huge amounts of (heterogeneous) data on the Web, the importance of information retrieval (IR) has grown considerably over the last few years. Big players in the computer industry, such as Google, Microsoft and Yahoo!, are the primary contributors of technology for fast access to Web-based information; and searching capabilities are now integrated into most information systems, ranging from business management software and customer relationship systems to social networks and mobile phone applications. Ceri and his co-authors aim at taking their readers from the foundations of modern information retrieval to the most advanced challenges of Web IR. To this end, their book is divided into three parts. The first part addresses the principles of IR and provides a systematic and compact description of basic information retrieval techniques (including binary, vector space and probabilistic models as well as natural language search processing) before focusing on its application to the Web. Part two addresses the foundational aspects of Web IR by discussing the general architecture of search engines (with a focus on the crawling and indexing processes), describing link analysis methods (specifically Page Rank and HITS), addressing recommendation and diversification, and finally presenting advertising in search (the main source of revenues for search engines). The third and final part describes advanced aspects of Web search, each chapter providing a self-contained, up-to-date survey on current Web research directions. Topics in this part include meta-search and multi-domain search, semantic search, search in the context of multimedia data, and crowd search. The book is ideally suited to courses on information retrieval, as it covers all Web-independent foundational aspects. Its presentation is self-contained and does not require prior background knowledge. It can also be used in the context of classic courses on data management, allowing the instructor to cover both structured and unstructured data in various formats. Its classroom use is facilitated by a set of slides, which can be downloaded from www.search-computing.org.

Order matters! Harnessing a world of orderings for reasoning over massive data

More and more applications require real-time processing of massive, dynamically generated, ordered data; order is an essential factor as it reflects recency or relevance. Semantic technologies risk being unable to meet the needs of such applications, as they are not equipped with the appropriate instruments for answering queries over massive, highly dynamic, ordered data sets. In this vision paper, we argue that some data management techniques should be exported to the context of semantic technologies, by integrating ordering with reasoning, and by using methods which are inspired by stream and rank-aware data management. We systematically explore the problem space, and point both to problems which have been successfully approached and to problems which still need fundamental research, in an attempt to stimulate and guide a paradigm shift in semantic technologies.