Mirco Nanni

Web log data warehousing and mining for intelligent web caching

Abstract We introduce intelligent web caching algorithms that employ predictive models of web requests; the general idea is to extend the least recently used (LRU) policy of web and proxy servers by making it sensitive to web access models extracted from web log data using data mining techniques. Two approaches have been studied in particular, frequent patterns and decision trees. The experimental results of the new algorithms show substantial improvement over existing LRU-based caching techniques, in terms of hit rate. We designed and developed a prototypical system, which supports data warehousing of web log data, extraction of data mining models and simulation of the web caching algorithms.

Probabilistic agent programs

Agents are small programs that autonomously take actions based on changes in their environment or “state”. Over the last few years, there has been an increasing number of efforts to build agents that can interact and/or collaborate with other agents. In one of these efforts Eiter et al. [1999] have shown how agents may be built on top of legacy code. However, their framework assumes that agent states are completely determined, and there is no uncertainty in an agents state. Thus, their framework allows an agent developer to specify how his agents will react when the agent is 100% sure about what is true/false in the world state. In this paper, we propose the concept of a probabilistic agent program and show how, given an arbitrary program written in any imperative language, we may build a declarative “probabilistic” agent program on top of it which supports decision making in the presence of uncertainty. We provide two alternative semantics for probabilitic programs. We provide sound and complete algorithms to compute the semantics of positive agent programs.

Data mining for intelligent Web caching

Presents a vertical application of data warehousing and data mining technology: intelligent Web caching. We introduce several ways to construct intelligent Web caching algorithms that employ predictive models of Web requests; the general idea is to extend the LRU (least recently used) policy of Web and proxy servers by making it sensible to Web access models extracted from Web log data using data mining techniques. Two approaches have been studied, in particular one based on association rules and another based on decision trees. The experimental results of the new algorithms show substantial improvements over existing LRU-based caching techniques in terms of the hit rate, i.e. the fraction of Web documents directly retrieved in the cache. We designed and developed a prototypical system, which supports data warehousing of Web log data, extraction of data mining models and simulation of the Web caching algorithms, around an architecture that integrates the various phases in the knowledge discovery process. The system supports a systematic evaluation and benchmarking of the proposed algorithms with respect to existing caching strategies.

Extracting trees of quantitative serial episodes

Among the family of the local patterns, episodes are commonly used when mining a single or multiple sequences of discrete events. An episode reflects a qualitative relation is-followed-by over event types, and the refinement of episodes to incorporate quantitative temporal information is still an on going research, with many application opportunities. In this paper, focusing on serial episodes, we design such a refinement called quantitative episodes and give a corresponding extraction algorithm. The three most salient features of these quantitative episodes are: (1) their ability to characterize main groups of homogeneous behaviors among the occurrences, according to the duration of the is-followed-by steps, and providing quantitative bounds of these durations organized in a tree structure; (2) the possibility to extract them in a complete way; and (3) to perform such extractions at the cost of a limited overhead with respect to the extraction of standard episodes.

Query Answering in Nondeterministic, Nonmonotonic Logic Databases

We consider in this paper an extension of Datalog with mechanisms for temporal, non monotonic and non deterministic reasoning, which we refer to as Datalog++. We show, by means of examples, its flexibility in expressing queries of increasing difficulty, up to aggregates and data cube. Also, we show how iterated fixpoint and stable model semantics can be combined to the purpose of clarifying the semantics of Datalog++ programs, and supporting their efficient execution. On this basis, the design of appropriate optimization techniques for Datalog++ is also briefly discussed.

On the Effective Semantics of Nondeterministic, Nonmonotonic, Temporal Logic Databases

We consider in this paper an extension of Datalog with mechanisms for temporal, nonmonotonic and nondeterministic reasoning, which we refer to as Datalog++. We study its semantics, and show how iterated fixpoint and stable model semantics can be combined to the purpose of clarifying the interpretation of Datalog++ programs, and supporting their efficient execution. On this basis, the design of appropriate optimization techniques for Datalog++ is also discussed.

Datalog++: A Basis for Active Object-Oriented Databases

We consider in this paper an extension of Datalog with mechanisms for temporal, non monotonic and non deterministic reasoning, which we refer to as Datalog++. First, we study its semantics, and show how iterated fixpoint and stable model semantics can be combined to the purpose of clarifying the interpretation of Datalog++ programs, and supporting their efficient execution. Second, we exhibit a compilation into Datalog++ of an active/deductive object-oriented model, ADOOD, including the schema definition language, the query language with multiple roles, the basic update operations, and a form of active rules. The proposed compilation is intended both to illustrate the expressiveness of Datalog++, and to provide a more flexible programming front-end to it. Finally, we illustrate the use of ADOOD by means of examples from semistructured data management.

Find Your Way Back: Mobility Profile Mining with Constraints

Mobility profile mining is a data mining task that can be formulated as clustering over movement trajectory data. The main challenge is to separate the signal from the noise, i.e. one-off trips. We show that standard data mining approaches suffer the important drawback that they cannot take the symmetry of non-noise trajectories into account. That is, if a trajectory has a symmetric equivalent that covers the same trip in the reverse direction, it should become more likely that neither of them is labelled as noise. We present a constraint model that takes this knowledge into account to produce better clusters. We show the efficacy of our approach on real-world data that was previously processed using standard data mining techniques.

Foundations of distributed interaction systems

There are numerous applications where a variety of human and software participants interactively pursue a given task (play a game, engage in a simulation, etc.). In this paper, we define a basic architecture for a distributed, interactive system (DIS for short). We then formally define a mathematical construct called a DIS abstraction that provides a theoretical basis for a software platform for building distributed interactive systems. Our framework provides a language for building multiagent applications where each agent has its own behaviors and where the behavior of the multiagent application as a whole is governed by one or more “master” agents. Agents in such a multiagent application may compete for resources, may attempt to take actions based on incorrect beliefs, may attempt to take actions that conflict with actions being concurrently attempted by other agents, and so on. Master agents mediate such conflicts. Our language for building agents (ordinary and master) depends critically on a notion called a “generalized constraint” that we define. All agents attempt to optimize an objective function while satisfying such generalized constraints that the agent is bound to preserve. We develop several algorithms to determine how an agent satisfies its generalized constraints in response to events in the multiagent application. We experimentally evaluate these algorithms in an attempt to understand their advantages and disadvantages.