Mining frequent subsequence patterns is a typical data-mining problem and various efficient sequential pattern mining algorithms have been proposed. In many application domains (e.g., biology), the frequent subsequences confined by the predefined gap requirements are more meaningful than the general sequential patterns. In this article, we propose two algorithms, Gap-BIDE for mining closed gap-constrained subsequences from a set of input sequences, and Gap-Connect for mining repetitive gap-constrained subsequences from a single input sequence. Inspired by some state-of-the-art closed or constrained sequential pattern mining algorithms, the Gap-BIDE algorithm adopts an efficient approach to finding the complete set of closed sequential patterns with gap constraints, while the Gap-Connect algorithm efficiently mines an approximate set of long patterns by connecting short patterns. We also present several methods for feature selection from the set of gap-constrained patterns for the purpose of classification and clustering. Our extensive performance study shows that our approaches are very efficient in mining frequent subsequences with gap constraints, and the gap-constrained pattern based classification/clustering approaches can achieve high-quality results.

As a typical data mining research topic, sequential pattern mining has been studied extensively for the past decade. Recently, mining various sequential patterns incrementally over stream data has raised great interest. Due to the challenges of mining stream data, many difficulties not so obvious in static data mining have to be reconsidered carefully. In this paper, we propose a novel algorithm which stores only frequent closed prefixes in its enumeration tree structure, used for mining and maintaining patterns in the current sliding window, to solve the frequent closed sequential pattern mining problem efficiently over stream data. Some effective search space pruning and pattern closure checking strategies have been also devised to accelerate the algorithm. Experimental results show that our algorithm outperforms other state-of-the-art algorithm significantly in both running time and memory use.

In this demonstration, we propose an interactive query completion system on structural data like DBLP, called SEQUEL. It is novel in several aspects: with patterns mined on the structural data using newly devised algorithm, SEQUEL offers high-utility completions composed with not only words but also phrases, and requires no explicit indications of corresponding columns. Instead of using query logs exploited previously for unstructured data, more effective completions are provided based on patterns mined directly from the records. Moreover, an effective index structure helps SEQUEL respond fast at millisecond level for each keystroke.

Web-page recommendation is to predict the next request of pages that Web users are potentially interested in when surfing the Web. This technique can guide Web users to find more useful pages without asking for them explicitly and has attracted much attention in the community of Web mining. However, few studies on Web page recommendation consider personalization, which is an indispensable feature to meet various preferences of users. In this paper, we propose a personalized Web page recommendation model called PIGEON (abbr. for PersonalIzed web paGe rEcommendatiON) via collaborative filtering and a topic-aware Markov model. We propose a graph-based iteration algorithm to discover users' interested topics, based on which user similarities are measured. To recommend topically coherent pages, we propose a topic-aware Markov model to learn users' navigation patterns which capture both temporal and topical relevance of pages. A thorough experimental evaluation conducted on a large real dataset demonstrates PIGEON's effectiveness and efficiency.

This paper describes our efforts in building an experimental platform to conduct research on cooperative driving in intelligent transportation systems (ITS). A miniature vehicle is developed based on the COTS-BOTS robot and an overhead vision system is used to localize the vehicles. The single lane tracking control algorithm is described first. To control multiple vehicles to drive through an intersection without collision, a distributed cooperative driving algorithm is developed, which is based on velocity planning using search algorithms in the corresponding coordination diagram. Experimental results verify our proposed approach and we believe this platform can serve as a cost-effective testbed to study vehicle-to-vehicle communication based cooperative driving in intelligent transportation systems

Information sharing through explicit communication is necessary in many multi-robot applications in order to achieve effective decision making. However, unnecessary large volumes of communication data usually lead to time delay and energy waste. This paper addresses the problem of how to efficiently synchronize the map (or explored area) among multiple robots when they carry out cooperative area exploration or coverage. In this application, multiple robots need exchange map information in order to minimize the repeated exploration or coverage. When a connected ad hoc network can not be maintained, the map synchronization problem becomes more challenging. In this paper, a sequence number based map representation scheme and an effective map update tracking scheme are proposed. Based on them, an algorithm is developed to reduce the volumes of map exchange when robot subnetworks merge. Simulation results validate this algorithm.

Safety of road travel can be increased if vehicles can be made to form groups for mutual interaction with each other. With the number of sensors available on vehicles increasing, a need has arisen to make collaborative use of the information collected by individual vehicles to form an enhanced dataset to improve the inter-vehicular safety features. This paper presents a novel approach to increase the safety of road travel using the concept of wireless sensor networks. We discuss how a group of vehicles can form a mobile ad hoc network and exchange data sensed by the on-board sensors. The fusion of these data could give a better understanding of the surrounding traffic conditions. As the simulation results show, this would definitely increase the traveling safety on the road.

This paper proposes a reliable and efficient multi-robot coordination algorithm to accomplish an area exploration task given that the communication range of each robot is limited. This algorithm is based on a distributed bidding model to coordinate the movement of multiple robots. Two measures are developed to accommodate the limited-range communications. First, the distances between robots are considered in the bidding algorithm so that the robots tend to stay close to each other. Second, a novel coding mechanism is introduced in the map representation to reduce the exchanged data volume when new communication links are formed. Simulation results show the effectiveness of the use of the nearness measure in the coordination algorithm as well as the new coding mechanism to reduce the amount of map data exchanged. By handling the limited communication range we can make the coordination algorithms more practical in multi-robot applications.