We investigate techniques for analysis and retrieval of object trajectories in two or three dimensional space. Such data usually contain a large amount of noise, that has made previously used metrics fail. Therefore, we formalize non-metric similarity functions based on the longest common subsequence (LCSS), which are very robust to noise and furthermore provide an intuitive notion of similarity between trajectories by giving more weight to similar portions of the sequences. Stretching of sequences in time is allowed, as well as global translation of the sequences in space. Efficient approximate algorithms that compute these similarity measures are also provided. We compare these new methods to the widely used Euclidean and time warping distance functions (for real and synthetic data) and show the superiority of our approach, especially in the strong presence of noise. We prove a weaker version of the triangle inequality and employ it in an indexing structure to answer nearest neighbor queries. Finally, we present experimental results that validate the accuracy and efficiency of our approach.

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Discovering similar multidimensional trajectories

Simulation and the monte carlo method

The conditions of learning

The coming years will witness dramatic advances in wireless communications as well as positioning technologies. As a result, tracking the changing positions of objects capable of continuous movement is becoming increasingly feasible and necessary. The present paper proposes a novel, R*-tree based indexing technique that supports the efficient querying of the current and projected future positions of such moving objects. The technique is capable of indexing objects moving in one-, two-, and three-dimensional space. Update algorithms enable the index to accommodate a dynamic data set, where objects may appear and disappear, and where changes occur in the anticipated positions of existing objects. A comprehensive performance study is reported.

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Indexing the positions of continuously moving objects

This introductory text on the design and analysis of sample surveys emphasizes the practical aspects of survey problems. It begins with brief chapters on the role of sample surveys in the modern world. Thereafter, each chapter introduces a sample survey design or estimation procedure by describing the pertinent practical problem. The authors describe the methodology proposed for solving the problem and provide the details of the estimation procedure, including a compact presentation of the formulas needed to complete the analysis. Then, a practical example is worked out in complete detail. At the end of each chapter, a wealth of exercises gives students ample opportunity to practice the techniques and stretch their grasp of ideas.

Elementary Survey Sampling

Presents the results from an extensive comparison study of three R-tree packing algorithms: the Hilbert and nearest-X packing algorithms, and an algorithm which is very simple to implement, called the STR (Sort-Tile-Recursive) algorithm The algorithms are evaluated using both synthetic and actual data from various application domains including VLSI design, GIS (Tiger files), and computational fluid dynamics Our studies also consider the impact that various degrees of buffering have on query performance Experimental results indicate that none of the algorithms as best for all types of data In general, our new algorithm requires up to 50% fewer disk accesses than the best previously proposed algorithm for point and region queries on uniformly distributed or mildly skewed point and region data, and approximately the same for highly skewed point and region data

STR: a simple and efficient algorithm for R-tree packing

Scheduling policies for general purpose multiprogrammed multiprocessors are not well understood. This paper examines various policies to determine which properties of a scheduling policy are the most significant determinants of performance. We compare a more comprehensive set of policies than previous work, including one important scheduling policy that has not previously been examined. We also compare the policies under workloads that we feel are more realistic than previous studies have used. Using these new workloads, we arrive at different conclusions than reported in earlier work. In particular, we find that the “smallest number of processes first” (SNPF) scheduling discipline performs poorly, even when the number of processes in a job is positively correlated with the total service demand of the job. We also find that policies that allocate an equal fraction of the processing power to each job in the system perform better, on the whole, than policies that allocate processing power unequally. Finally, we find that for lock access synchronization, dividing processing power equally among all jobs in the system is a more effective property of a scheduling policy than the property of minimizing synchronization spin-waiting, unless demand for synchronization is extremely high. (The latter property is implemented by coscheduling processes within a job, or by using a thread management package that avoids preemption of processes that hold spinlocks.) Our studies are done by simulating abstract models of the system and the workloads.

The performance of multiprogrammed multiprocessor scheduling algorithms

In this paper we argue for using a "Game First" approach to teaching introductory programming. We believe that concerns over whether an OO approach or a procedural approach should be used first are secondary to the course assignment and example content. If examples are not compelling, student interest often lags thus making the OO versus procedural argument moot. We believe that game programming motivates most new programmers. Compelling assignments mean that students are far more likely to learn because they are interested, and the visual component allows students to see mistakes in their code as manifested in the resultant graphics. We describe our experiences after redesigning and offering a new introductory computer science sequence using 2D game development as a unifying theme. We teach fundamental programming concepts via two dimensional game development in Flash and ActionScript during the first quarter, transition to C++ to solidify concepts and add pointers during the second quarter, then teach a multi-phase project based game approach using C++ and openGL (2D graphics only) during the third quarter. Our surveys show that this approach improved student understanding of all seven basic topics examined.

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A games first approach to teaching introductory programming

The evaluation of parallel job schedulers hinges on the workloads used. It is suggested that this be standardized, in terms of both format and content, so as to ease the evaluation and comparison of different systems. The question remains whether this can encompass both traditional parallel systems and metacomputing systems.

This paper is based on a panel on this subject that was held at the workshop, and the ensuing discussion; its authors are both the panel members and participants from the audience. Naturally, not all of us agree with all the opinions expressed here...

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Scott T. Leutenegger

Papers

Indexing the positions of continuously moving objects

STR: a simple and efficient algorithm for R-tree packing

The performance of multiprogrammed multiprocessor scheduling algorithms

A games first approach to teaching introductory programming

Benchmarks and Standards for the Evaluation of Parallel Job Schedulers