Sandra de Amo

A paraconsistent logic programming approach for querying inconsistent databases

When integrating data coming from multiple different sources we are faced with the possibility of inconsistency in databases. A paraconsistent approach for knowledge base integration allows keeping inconsistent information and reasoning in its presence. In this paper, we use a paraconsistent logic (LFI1) as the underlying logic for the specification of P-Datalog, a deductive query language for databases containing inconsistent information. We present a declarative semantics which captures the desired meaning of a recursive query executed over a database containing inconsistent facts and whose rules allow inferring information from inconsistent premises. We also present a bottom-up evaluation method for P-Datalog programs based on an alternating fixpoint operator.

First-order temporal pattern mining with regular expression constraints

Previous studies on mining sequential patterns have focused on temporal patterns specified by some form of propositional temporal logic. However, there are some interesting sequential patterns, such as the multi-sequential patterns, whose specification needs a more expressive formalism, the first-order temporal logic. Multi-sequential patterns appear in different application contexts, for instance in spatial census data mining, which is the target application of the study developed in this paper. We extend a well-known user-controlled tool, based on regular expressions constraints, to the multi-sequential pattern context. This specification tool enables the incorporation of user focus into the mining process. We present MSP-Miner, an Apriori-based algorithm to discover all frequent multi-sequential patterns satisfying a user-specified regular expression constraint.

MILPRIT*: A Constraint-Based Algorithm for Mining Temporal Relational Patterns

In this article, we consider a new kind of temporal pattern where both interval and punctual time representation are considered. These patterns, which we call temporal point-interval patterns, aim at capturing how events taking place during different time periods or at different time instants relate to each other. The datasets where these kinds of patterns may appear are temporal relational databases whose relations contain point or interval timestamps. We use a simple extension of Allen’s Temporal Interval Logic as a formalism for specifying these temporal patterns. We also present the algorithm MILPRIT* for mining temporal point-interval patterns, which uses variants of the classical levelwise search algorithms. In addition, MILPRIT* allows a broad spectrum of constraints to be incorporated into the mining process. An extensive set of experiments of MILPRIT* executed over synthetic and real data is presented, showing its effectiveness for mining temporal relational patterns.

Mining contextual preference rules for building user profiles

The emerging of ubiquitous computing technologies in recent years has given rise to a new field of research consisting in incorporating context-aware preference querying facilities in database systems. One important step in this setting is the Preference Elicitation task which consists in providing the user ways to inform his/her choice on pairs of objects with a minimal effort. In this paper we propose an automatic preference elicitation method based on mining techniques. The method consists in extracting a user profile from a set of user preference samples. In our setting, a profile is specified by a set of contextual preference rules verifying properties of soundness and conciseness. We evaluate the efficacy of the proposed method in a series of experiments executed on a real-world database of user preferences about movies.

Contextual preference mining for user profile construction

The emerging of ubiquitous computing technologies in recent years has given rise to a new field of research consisting in incorporating context-aware preference querying facilities in database systems. One important step in this setting is the Preference Elicitation task which consists in providing the user ways to inform his/her choice on pairs of objects with a minimal effort. In this paper we propose an automatic preference elicitation method based on mining techniques. The method consists in extracting a user profile from a set of user preference samples. In our setting, a profile is specified by a set of contextual preference rules verifying properties of soundness and conciseness. After proving that the problem is NP-complete, we propose a resolution in 2 phases. The first phase extracts all individual user preferences by means of contextual preference rules. The second phase builds the user profile starting from this collection of rules using a greedy method. To assess the quality of user profiles, we propose three ranking techniques benefiting from these profiles that enable us to rank objects according to user preferences. We evaluate the efficacy of our three ranking strategies and compare them with a well-known ranking method (SVMRank). The evaluation is carried out through an extensive set of experiments executed on a real-world database of user preferences about movies. HighlightsWe propose a new method for mining user profiles from preference databases.Our method is the first one based on pattern mining techniques.It builds readable user profile based on the notion of contextual preference rules.Three ranking techniques are proposed to rank objects according to a user profile.A set of experiments on a real-world database showed the efficiency of the method.

CPref-SQL: a query language supporting conditional preferences

Nowadays, the need for incorporating preference querying in database technology is a very important issue in a variety of applications ranging from e-commerce to personalized search engines. A lot of recent research work has been dedicated to this topic in the artificial intelligence and database communities. Several formalisms allowing preference reasoning and specification have been proposed in the AI field. On the other hand, in the database field the interest has been focused mainly in extending standard SQL with preference facilities in order to provide personalized query answering. In this paper, we propose to build a bridge between these two approaches, by using a logic formalism originally designed to specify and reason with preference in order to extend SQL with conditional preference constructors. Such constructors allow to express a large class of preference statements with a ceteris-paribus semantics.

CPrefMiner: An Algorithm for Mining User Contextual Preferences Based on Bayesian Networks

In this article we propose CPrefMiner, a mining technique for learning a Bayesian Preference Network (BPN) from a given sample of user choices. In our approach, user preferences are not static and may vary according to a multitude of user contexts. So, we name them Contextual Preferences. Contextual Preferences can be naturally expressed by a BPN. The method has been evaluated in a series of experiments executed on synthetic and real-world datasets and proved to be efficient to discover user contextual preferences.

Top-k Context-Aware Queries on Streams

Preference queries have been largely studied for relational systems but few proposals exist for stream data systems. Most of the existing proposals concern the skyline, top-k or top-k dominating queries, coupled with the sliding-window operator. However, user preferences queries on data streams may be more sophisticated than simple skyline or top-k and may involve more expressive operations on streams. This paper improves the existing work on data stream query-answering personalization by proposing a solution to express and handle contextual preferences together with a large variety of queries including one-shot and continuous queries. It adopts a more expressive preference model supporting context-based preferences, allowing to capture a wide range of situations. We propose algorithms to implement the new preference operators on stream data and validate their performance on a real-world dataset of stock market streams.

Classroom Experience: A Platform for Multimedia Capture and Access in Instrumented Educational Environments

Capture and access applications are developed to support the recording and later recall of multimedia information. The capture phase demands complex mechanisms for the acquisition and synchronization of user-generated data, while the access phase requires infrastructures that allow the personalization of the captured content. In this paper, we present a ubiquitous computing platform for the capture and access of educational activities in an instrumented classroom. Our approach uses context information in order to personalize the access experience according to users preferences. Transparent communication mechanisms complement our proposal by supporting data transfer and synchronization among associated storage services.

On Using Temporal Networks to Analyze User Preferences Dynamics

User preferences are fairly dynamic, since users tend to exploit a wide range of information and modify their tastes accordingly over time. Existing models and formulations are too constrained to capture the complexity of this underlying phenomenon. In this paper, we investigate the interplay between user preferences and social networks over time. We propose to analyze user preferences dynamics with his/her social network modeled as a temporal network. First, we define a temporal preference model for reasoning with preferences. Then, we use evolving centralities from temporal networks to link with preferences dynamics. Our results indicate that modeling Twitter as a temporal network is more appropriated for analyzing user preferences dynamics than using just snapshots of static network.