Jafar Adibi

Expressing and optimizing sequence queries in database systems

The need to search for complex and recurring patterns in database sequences is shared by many applications. In this paper, we investigate the design and optimization of a query language capable of expressing and supporting efficiently the search for complex sequential patterns in database systems. Thus, we first introduce SQL-TS, an extension of SQL to express these patterns, and then we study how to optimize the queries for this language. We take the optimal text search algorithm of Knuth, Morris and Pratt, and generalize it to handle complex queries on sequences. Our algorithm exploits the interdependencies between the elements of a pattern to minimize repeated passes over the same data. Experimental results on typical sequence queries, such as double bottom queries, confirm that substantial speedups are achieved by our new optimization techniques.

Optimization of sequence queries in database systems

The need to search for complex and recurring patterns in database sequences is shared by many applications. In this paper, we discuss how to express and support efficiently sophisticated sequential pattern queries in databases. Thus, we first introduce SQL-TS, an extension of SQL, to express these patterns, and then we study how to optimize search queries for this language. We take the optimal text search algorithm of Knuth, Morris and Pratt, and generalize it to handle complex queries on sequences. Our algorithm exploits the inter-dependencies between the elements of a sequential pattern to minimize repeated passes over the same data. Experimental results on typical sequence queries, such as double bottom queries, confirm that substantial speedups are achieved by our new optimization techniques.

Processing-in-memory technology for knowledge discovery algorithms

The goal of this work is to gain insight into whether processing-in-memory (PIM) technology can be used to accelerate the performance of link discovery algorithms, which represent an important class of emerging knowledge discovery techniques. PIM chips that integrate processor logic into memory devices offer a new opportunity for bridging the growing gap between processor and memory speeds, especially for applications with high memory-bandwidth requirements. As LD algorithms are data-intensive and highly parallel, involving read-only queries over large data sets, parallel computing power extremely close (physically) to the data has the potential of providing dramatic computing speedups. For this reason, we evaluated the mapping of LD algorithms to a processing-in-memory (PIM) workstation-class architecture, the DIVA/Godiva hardware testbeds developed by USC/ISI. Accounting for differences in clock speed and data scaling, our analysis shows a performance gain on a single PIM, with the potential for greater improvement when multiple PIMs are used. Measured speedups of 8x are shown on two additional bandwidth benchmarks, even though the Itanium-2 has a clock rate 6X faster.

On being a teammate: experiences acquired in the design of RoboCup teams

Increasingly, multi-agent systems are being designed for a variety of complex, dynamic domains. Effective agent interactions in such domains raise some of the most fundamental research challenges for agent-based systems, in teamwork, multi-agent learning and agent modelling. The RoboCup research initiative, particularly the simulation league, has been proposed to pursue such multi-agent research challenges, using the common testbed of simulation soccer. Despite the significant popularity of RoboCup within the research community, general lessons have not often been extracted from participation in RoboCup. This is what we attempt to do here. We have fielded two teams, ISIS97 and ISIS98, in RoboCup competitions. These teams have been in the top four teams in these competitions. We compare the teams, and attempt to analyze and generalize the lessons learned. This analysis reveals several surprises, pointing out lessons for teamwork and for multi-agent learning.

Analysis and design of server informative WWW-sites

The access patterns of the users on a web site are traditionally investigated in order to improve the user access to the site s information In this study however a systematic approach is introduced in order to analyze the users navigation path to the advantage of the web site owner As users navigate through a web site they are transparently lling a questionnaire generated by the web site owner We rst cluster the users who navigate similar paths employing the Path Mining algorithm Next the correlation between a set of target questions and the structure of the WWW site is quanti ed This has been done by borrowing the concept of channel from information theory A channel can be considered as an information bridge between the users path classes and the answers to a questionnaire By adopting many concepts from information theory we introduce a natural measure to compute the e ectiveness of a WWW site structure in answering the target questionnaire Using this measure we provide a set of design guidelines to make WWW sites more informative for the server To nd the parameters of a channel we propose a learning process based on a set of training data and or inputs from a human expert Finally our proposed approach is tested on a sample WWW site and the results demonstrate dramatic improvement in the server information passing Author s work supported by NSF grant EEC IMSC ERC

Building integrated mobile robots for soccer competition

Robot soccer competition provides an excellent opportunity for robotics research. In particular, robot players in a soccer game must perform real-time visual recognition, navigate in a dynamic field, track moving objects, collaborate with teammates, and strike the ball in the correct direction. All these tasks demand robots that are autonomous (sensing, thinking, and acting as independent creatures), efficient (functioning under time and resource constraints), cooperative (collaborating with each other to accomplish tasks that are beyond individuals capabilities), and intelligent (reasoning and planing actions and perhaps learning from experience). Furthermore, all these capabilities must be integrated into a single and complete system. To build such integrated robots, we should use different approaches from those employed in separate research disciplines. This paper describes our experience (problems and solutions) in this aspect for building soccer robots. Our robots share the same general architecture and basic hardware, but they have integrated abilities to play different roles and utilize different strategies in their behavior. Our philosophy in building these robots is to use the least possible sophistication to make them as robust as possible. In RoboCup97, our Dreamteam robots performed well (scored 8 of 9 goals of all teams in the league) and won the world championship in the middle-sized robot league.

Experiences Acquired in the Design of RoboCup Teams: A Comparison of Two Fielded Teams

Increasingly, multi-agent systems are being designed for a variety of complex, dynamic domains. Effective agent interactions in such domains raise some of the most fundamental research challenges for agent-based systems, in teamwork, multi-agent learning and agent modelling. The RoboCup research initiative, particularly the simulation league, has been proposed to pursue such multi-agent research challenges, using the common testbed of simulation soccer. Despite the significant popularity of RoboCup within the research community, general lessons have not often been extracted from participation in RoboCup. This is what we attempt to do here. We have fielded two teams, ISIS97 and ISIS98, in RoboCup competitions. These teams have been in the top four teams in these competitions. We compare the teams, and attempt to analyze and generalize the lessons learned. This analysis reveals several surprises, pointing out lessons for teamwork and for multi-agent learning.

Self-Similar Layered Hidden Markov Models

Hidden Markov Models (HMM) have proven to be useful in a variety of real world applications where considerations for uncertainty are crucial. Such an advantage can be more leveraged if HMM can be scaled up to deal with complex problems. In this paper, we introduce, analyze and demonstrate Self-Similar Layered HMM (SSLHMM), for a certain group of complex problems which show self-similar property, and exploit this property to reduce the complexity of model construction. We show how the embedded knowledge of self-similar structure can be used to reduce the complexity of learning and increase the accuracy of the learned model. Moreover, we introduce three different types of self-similarity in SSLHMM, and investigate their performance in the context of synthetic data and real-world network databases. We show that SSLHMM has several advantages comparing to conventional HMM techniques and it is more efficient and accurate than one-step, flat method for model construction.

Toward Integrated Soccer Robots

Robot soccer competition provides an excellent opportunity for integrated robotics research. In particular, robot players in a soccer game must recognize and track objects in real time, navigate in a dynamic field, collaborate with teammates, and strike the ball in the correct direction. All these tasks demand robots that are autonomous (sensing, thinking, and acting as independent creatures), efficient (functioning under time and resource constraints), cooperative (collaborating with each other to accomplish tasks that are beyond an individuals capabilities), and intelligent (reasoning and planning actions and perhaps learning from experience). Furthermore, all these capabilities must be integrated into a single and complete system, which raises a set of challenges that are new to individual research disciplines. This article describes our experience (problems and solutions) in these aspects. Our robots share the same general architecture and basic hardware, but they have integrated abilities to play different roles (goalkeeper, defender, or forward) and use different strategies in their behavior. Our philosophy in building these robots is to use the least sophistication to make them as robust and integrated as possible. At RoboCup-97, held as part of the Fifteenth International Joint Conference on Artificial Intelligence, these integrated robots performed well, and our DREAMTEAM won the world championship in the middle-size robot league.

Yoda: The Young Observant Discovery Agent

The YODA Robot Project at the University of Southern California/Information Sciences Institute consists of a group of young researchers who share a passion for autonomous systems that can bootstrap its knowledge from real environments by exploration, experimentation, learning, and discovery. Our goal is to create a mobile agent that can autonomously learn from its environment based on its own actions, percepts, and mis-sions. Our participation in the Fifth Annual AAAI Mobile Robot Competition and Exhibition, held as part of the Thirteenth National Conference on Artificial Intelligence, served as the first milestone in advancing us toward this goal. YODAs software architecture is a hierarchy of abstraction layers, ranging from a set of behaviors at the bottom layer to a dynamic, mission-oriented planner at the top. The planner uses a map of the environment to determine a sequence of goals to be accomplished by the robot and delegates the detailed executions to the set of behaviors at the lower layer. This abstraction architecture has proven robust in dynamic and noisy environments, as shown by YODAs performance at the robot competition.