1387834
An Efficient Algorithm for Pattern Discovery in Large Text Databases
5115871
Temporal Knowledge Discovery with Infrequent Episodes
In this paper, we present an efficient algorithm which discovers rare episodes with a combination of bottom-up and top-down scanning schema. The information sharing between bottom-up and top-down scannings helps prune candidate episodes, and thus, efficiently find infrequent episodes that are interesting to user: We evaluate the performance of the algorithm using real-life weather databases. We observe from experimental results that our approach results in 30%-90% reduction in computation time and 25%-75% reduction in the number of candidates comparing with Apriori algorithm.
38979960
Interpolation Techniques for Geo-spatial Association Rule Mining
Association rule mining has become an important component of information processing systems due to significant increase in its applications. In this paper, our main objective is to find which interpolation approaches are best suited for discovering geo-spatial association rules from unsampled points. We investigate and integrate two interpolation approaches into our geo-spatial association rule mining algorithm. We call them pre-interpolation and post-interpolation approaches.
34229141
Spatio-Temporal Association Mining for Un-sampled Sites
In this paper, we investigate interpolation methods that are suitable for discovering spatio-temporal association rules for unsampled points with an initial focus on drought risk management. For drought risk management, raw weather data is collected, converted to various indices, and then mined for association rules. To generate association rules for unsampled sites, interpolation methods can be applied at any stage of this data mining process. We develop and integrate three interpolation models into our association rule mining algorithm. The performance of these three models is experimentally evaluated comparing interpolated association rules with rules discovered from actual raw data.
17075751
A collaboration specification language
COCA (Collaborative Objects Coordination Architecture) was proposed as a novel means to model and support collaborations over the Internet. Our approach separates coordination policies from user interfaces and the policies are specified in a logic-based language. Over the past year, both the collaboration model and the specification language have been substantially refined and evaluated through our experience in building real-life collaboration systems. This paper presents the design of the specification language and illustrates the main ideas with a few simple design examples. Semantics, implementation, runtime support, and applications are also covered but not as the focus of this paper.
6720899
Mode selection and mode-dependency modeling for power-aware embedded systems
Among the techniques for system-level power management, it is not currently possible to guarantee timing constraints and have a comprehensive system model supporting multiple components at the same time. We propose a new method for modeling and selecting the power modes for the optimal system-power management of embedded systems under timing and power constraints. First, we not only model the modes and the transitions overhead at the component level, but we also capture the application-imposed relationships among the components by introducing a mode dependency graph at the system level. Second, we propose a mode selection technique, which determines when and how to change mode in these components such that the whole system can meet all power and timing constraints. Our constraint-driven approach is a critical feature for exploring power/performance tradeoffs in power-aware embedded systems. We demonstrate the application of our techniques to a low-power sensor and an autonomous rover.
7717414
A time interval based consistency control algorithm for interactive groupware applications
Traditional concurrency control methods such as locking and serialization are not suitable for distributed interactive applications that demand fast local response. Operational transformation (OT) is the standard solution to concurrency control and consistency maintenance in group editors, an important class of interactive groupware applications. It generally trades consistency for local responsiveness, because human users can often tolerate temporary inconsistencies but do not like their interactions be lost or nondeterministically blocked. This paper presents a time interval based operational transformation algorithm (TIBOT) that overcomes the various limitations of previous related work. Our approach guarantees content convergence and is significantly more simple and efficient than existing approaches. This is achieved in a pure replicated architecture by using a linear clock and by posing some constraints on communication that are reasonable for the application domain.
46474884
Explicit Efficient Frontier of a Continuous-Time Mean Variance Portfolio Selection Problem
In this work we consider a continuous-time mean-variance portfolio selection problem that is formulated as a bi-criteria optimization problem. The objective is to maximize the expected return and minimize the variance of the terminal wealth. By putting weights on the two criteria one obtains a single objective stochastic control problem which is however not in the standard form. We show that this non-standard problem can be “embedded” into a class of auxiliary stochastic linear-quadratic (LQ) problems. By solving the latter based on the recent development on stochastic LQ problems with indefinite control weighting matrices, we derive the efficient frontier in a closed form for the original mean-variance problem.
36670641
A nonlinear Lagrangian dual for integer programming
Nonlinear Lagrangian theory offers a success guarantee for the dual search via construction of a nonlinear support of the perturbation function at the optimal point. In this paper, a new nonlinear dual formulation of an exponential form is proposed for bounded integer programming. This new formulation possesses an asymptotic strong duality property and guarantees a success in identifying a primal optimum solution. No actual dual search is needed in the solution process when the parameter of the nonlinear Lagrangian formulation is set to be large enough.