Computing and Information Systems - Theses
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ItemTowards small-effort adaptions to off-the-shelf spatial and temporal indexes for modern database applications( 2012)Modern database applications demand high throughput of queries on complex data, such as the increasingly common types of spatial and temporal data. Many structures have been proposed for indexing spatial and temporal data and as standalone implementations they often achieve high levels of performance. However they are generally difficult to implement in an existing DBMS, which makes them very expensive to adopt. For example, Oracle took more than 5 years to implement the R-tree spatial index structure in their commercial DBMS. This thesis examines the techniques that exist in off-the-shelf spatial and temporal indexes with an emphasis on achieving large performance gains with minimal changes. In the domain of spatial data we propose a new index structure called Size Separation Indexing (SSI). This structure builds on the B+-tree, which is present in almost all DBMSs. Through extensive experimentation we show that SSI performs at least as well as all current spatial indexes and better than most on both a flat filesystem and on top of a DBMS. In the domain of temporal data we extend the TSB-tree, which is also based on the B+ -tree. In recent work the TSB-tree has been integrated into Microsoft SQL Server and we introduce the memory Hierarchy-aware Version tree which significantly improves on the performance of the TSB-tree for almost all query types and is scalable to huge datasets. Because our structures build upon existing indexes, we argue that they are better suited for implementation in current systems than other structures, reducing costs and increasing performance.
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ItemOnline time series approximation and prediction( 2012)In recent years, there are rapidly increasing research interests in the management of time series data due to its importance in a variety of applications such as network traffic management, telecommunications, finance, sensor network and location based services. In this thesis, we focus on two important problems of the management of time series data: the online time series approximation problem and the online time series prediction problem. The time series approximation can reduce the space and the computational cost of storing and transmitting time series data, and also reduce the workload of the data processing. Segmentation is one of the most commonly used methods to meet this requirement. However, while most of the current segmentation methods aim to minimize the holistic error between the approximation and the original time series, few works try to represent time series as compact as possible with an error bound guarantee on each data point. Moreover, in many real world situations, the patterns of the time series do not follow a constant rule such that using only one type of functions may not yield the best compaction. Motivated by these observations, we propose an online segmentation algorithm which approximates time series by a set of different types of candidate functions (poly nomials of different orders, exponential functions, etc.) and adaptively chooses the most compact one as the pattern of the time series changes. A challenge in this approach is to determine the approximation function on the fly (“online”). Thereby, we further present a novel method to efficiently generate the compact approximation of a time series in an online fashion for several types of candidate functions. This method incrementally narrows the feasible coefficient spaces of candidate functions in coefficient coordinate systems such that it can make each segment as long as possible given an error bound on each data point. Extensive experimental results show that our algorithm generates more compact approximations of the time series with lower average errors than the state-of-the-art algorithm. The time series prediction aims to predict future values of the time series according to their previously observed values. In this thesis, we focus our work on a specific branch of the time series prediction: the online locational time series prediction problem, i.e, predicting the final locations of the locational time series according to their current observed locational time series values on the fly. This problem is also called the destination prediction problem and the systems used to solve this problem are the called destination prediction systems. However, most of current destination prediction systems are based on the traveling histories of specific users so they are too ad hoc to accurately predict other users’ destination. In our work, we propose the first generic destination prediction solution to provide the accurate destination prediction services to all users based on the given user queries (i.e., the users’ current partial traveling trajectories) without knowing any traveling histories of the users. Extensive experiments validate that the prediction result of our method is more accurate than that of the competing method.
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ItemA generic framework for the simulation of biologically plausible spiking neural networks on graphics processors( 2011)The study of the structure and functionality of the brain has been ardently investigated, as the implications of such research may aid in the treatment and diagnosis of mental diseases. This has led to a growing interest in numerical simulation tools that can model its network complexity, in order to achieve a greater understanding of the underlying processes of this complex biological system. The computational requirements of neural modeling makes high performance multi-core systems a desirable architecture when simulating large-scale networks. Graphics processing units (GPUs) are an inexpensive, power-efficient, supercomputing alternative for solving compute-intensive scientific applications. However, the irregular communication and execution patterns in realistic spiking neural networks pose a challenge to their implementation on these massively data parallel devices. In this work, we propose a generic framework for simulating large-scale spiking neural networks with biologically realistic connectivity on GPUs. We provide an extensive list of optimization techniques and strategies which target the main issues involved with neural simulation on these devices, such as: optimal access patterns, synaptic referencing, current aggregation, firing representation, and task distribution. We succeed in building a GPU-based simulator that preserves the flexibility, accuracy, and biological plausibility of neural simulation, while providing high performance and efficient memory usage. Overall, our implementation achieves speedups of around 35-84 times on a single graphics card over an optimized CPU implementation based on the SPIKESIM simulator. We also provide a comparison with other GPU neural simulators related to this work. Following that, we analyze the communication aspects of migrating the system onto a multi-GPU cluster. This is done in an attempt to quantitatively determine the implications of communication overhead with large-scale neural simulation, when employing distributed clusters with GPU devices. We describe a model to determine the arising dependency cost from partitioning a neural network across the different components of the distributed system. We also discuss various techniques for minimizing overhead resulting from frequent messaging and global synchronization. Finally, we provide a theoretical analysis of the suggested communication model in relation to computational and overall performance, as well as a discussion on the relevance of the work.
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ItemMulti-resolution indexing method for time series( 2010)Time series datasets are useful in a wide range of diverse real world applications. Retrieving or querying from a collection of time series is a fundamental task, with a key example being the similarity query. A similarity query returns all time series from the collection that are similar to a given reference time series. This type of query is particularly useful in prediction and forecasting applications. A key challenge for similarity queries is efficiency and for large datasets, it is important to develop efficient indexing techniques. Existing approaches in this area are mainly based on the Generic Multimedia Indexing Method (GEMINI), which is a framework that uses spatial indexes such as the R-tree to index reduced time series. For processing a similarity query, the index is first used to prune candidate time series using a lower bounding distance. Then, all remaining time series are compared using the original similarity measure, to derive the query result. Performance within this framework depends on the tightness of the lower bounding distance with respect to the similarity measure. Indeed much work has been focused on representation and dimensionality reduction, in order to provide a tighter lower bounding distance. Existing work, however, has not used employed dimensionality reduction in a flexible way, requiring all time series to be reduced to have the same dimension. In contrast, in this thesis, we investigate the possibility of allowing a variable dimension reduction. To this end, we develop a new and more flexible tree based indexing structure called the Multi-Resolution Index (MR-Index), which allows dimensionality to vary across different levels of the tree. We provide efficient algorithms for querying, building and maintaining this structure. Through an experimental analysis, we show that the MR-Index can deliver improved query efficiency compared to the traditional R-tree index, using both the Euclidean and dynamic time warping similarity measures.