1362728
Structural join order selection for XML query optimization
Structural join operations are central to evaluating queries against XML data, and are typically responsible for consuming a lion's share of the query processing time. Thus, structural join order selection is at the heart of query optimization in an XML database, just as (value-based) join order selection is central to relational query optimization. We introduce five algorithms for structural join order optimization for XML tree pattern matching and present an extensive experimental evaluation. Our experiments demonstrate that many relational rules of thumb are no longer appropriate: for instance, using dynamic programming style optimization is not efficient; limiting consideration to left-deep plans usually misses the best solution. Our experiments also show that a dynamic programming optimization with pruning (DPP) algorithm can find the optimal solution, with low cost relative to the traditional dynamic programming (DP) algorithm; and an optimization technique that only considers fully pipelined (FP) plans can very quickly choose a plan that in most cases is close to optimal. Our recommendation is that DPP should be used in XML query optimizers where query execution time is expected to be significant, and that FP should be used where it is important to find a good (but not necessarily the best) plan quickly.
7798629
Structural joins: a primitive for efficient XML query pattern matching
XML queries typically specify patterns of selection predicates on multiple elements that have some specified tree structured relationships. The primitive tree structured relationships are parent-child and ancestor-descendant, and finding all occurrences of these relationships in an XML database is a core operation for XML query processing. We develop two families of structural join algorithms for this task: tree-merge and stack-tree. The tree-merge algorithms are a natural extension of traditional merge joins and the multi-predicate merge joins, while the stack-tree algorithms have no counterpart in traditional relational join processing. We present experimental results on a range of data and queries using the TIMBER native XML query engine built on top of SHORE. We show that while, in some cases, tree-merge algorithms can have performance comparable to stack-tree algorithms, in many cases they are considerably worse. This behavior is explained by analytical results that demonstrate that, on sorted inputs, the stack-tree algorithms have worst-case I/O and CPU complexities linear in the sum of the sizes of inputs and output, while the tree-merge algorithms do not have the same guarantee.
669184
Generality of Texts
When searching or browsing, a user may be looking for either very general information or very specific information. We explored predictors for characterizing the generality of six encyclopedia texts. We had human subjects rank-order the generality of the texts. We also developed statistics from analysis of word frequency and from comparison to a set of reference terms. We found a statistically significant relationship between the human ratings of text generality and our automatic measure.
15058650
Query estimation by adaptive sampling
The ability to provide accurate and efficient result estimations of user queries is very important for the query optimizer in database systems. In this paper, we show that the traditional estimation techniques with data reduction points of view do not produce satisfiable estimation results if the query patterns are dynamically changing. We further show that to reduce query estimation error, instead of accurately capturing the data distribution, it is more effective to capture the user query patterns. In this paper, we propose query estimation techniques that can adapt to user query patterns for more accurate estimates of the size of selection or range queries over databases.
1673387
Two-Stage Optimal Component Analysis
Linear representations are widely used to reduce dimension in applications involving high dimensional data. While specialized procedures exist for certain optimality criteria, such as principle component analysis (PCA) and Fisher discriminant analysis (FDA), they can not be generalized for more general criteria. To overcome this fundamental limitation, optimal component analysis (OCA) uses a stochastic gradient optimization procedure intrinsic to the manifold giving by the constraints of applications and therefore gives a procedure for finding optimal representations for general criteria. However, due to its generality nature, OCA often requires extensive computation for gradient estimation and updating. To significantly reduce the required computation, in this paper, we propose a two-stage method by first reducing the dimension of input to a smaller one (but larger than the final resulting dimension) using a computationally efficient method and then performing OCA in the reduced space. This reduces the computation time from days to minutes on widely used databases, making OCA learning feasible for many applications. Additionally, since the reduced space is much smaller, the stochastic gradient optimization tends to be more efficient. We illustrate the effectiveness of the proposed method on face classification.
10558586
A dual-mode built-in self-test technique for capacitive MEMS devices
A dual-mode built-in self-test (BIST) scheme which partitions the fixed (instead of movable) capacitance plates of a capacitive microelectromechanical system (MEMS) device is proposed. The BIST technique divides the fixed capacitance plate(s) at each side of the movable microstructure into three portions: one for electrostatic activation and the other two equal portions for capacitance sensing. Due to such a partitioning method, the BIST technique can be applied to surface- and bulk-micromachined MEMS devices and other technologies. Further, the sensitivity and symmetry dual BIST modes based on this partitioning can also be developed. The combination of both BIST modes covers a larger defect set, so a more robust testing result for the device can be expected. The BIST technique is verified by three typical capacitive MEMS devices. Simulation results show that the proposed technique is an effective BIST solution for various capacitive MEMS devices.
11120101
A comparison of DFT and DWT based similarity search in time-series databases
Similarity searc h in time-series databases has received signi cant attention lately .P opular tec hniques for e cien t retrieval of time sequences in time-series databases has been to use Discrete Fourier Transform (DFT). Recently, the Discrete Wavelet T ransform (DWT) has gained popular interest in database domain and several proposals have been made to replace DFT by DWT for similarity search over time-series databases. In this paper, we explore the feasibility of replacing DFT by DWT with a comprehensive analysis of the DFT and DWT as matching functions in time-series databases. Our results show that although the DWT based technique has several adv an tages,e.g., the D WThas complexity of O(N) whereas DFT is O(N logN), D WT does not reduce relativ e matching error and does not increase query precision in similarity searc h as suggested by previous works [1]. We conclude that, by exploring the conjugate property of DFT in real domain, the DFT-based and DWT-based techniques yield comparable results on similarity searc h in time-series databases.