612178
A learning-based approach to estimate statistics of operators in continuous queries: a case study
Statistic estimation such as output size estimation of operators is a well-studied subject in the database research community, mainly for the purpose of query optimization. The assumption, however, is that queries are ad-hoc and therefore the emphasis has been on capturing the data distribution. When long standing continuous queries on a changing database are concerned, a more direct approach, namely building an estimation model for each operator, is possible. In this paper, we propose a novel learning-based method. Our method consists of two steps. The first step is to design a dedicated feature extraction algorithm that can be used incrementally to obtain feature values from the underlying data. The second step is to use a data mining algorithm to generate an estimation model based on the feature values extracted from the historical data. To illustrate the approach, this paper studies the case of similarity-based searches over streaming time series. Experimental results show this approach provides accurate statistic estimates with a low overhead.
6594519
Using triangle inequality to efficiently process continuous queries on high-dimensional streaming time series
In many applications, it is important to quickly find, from a database of patterns, the nearest neighbors of high-dimensional query points that come into the system in a streaming form. Treating each query point as a separate one is inefficient. Consecutive query points are often neighbors in the high-dimensional space, and intermediate results in the processing of one query should help the processing of the next. This paper extends the KD tree with triangle inequality to deal with high-dimensional streaming time series. More specifically, the distances calculated for earlier query points (to patterns) are used to filter out patterns that are not possible to be the nearest neighbor of the current one. Experiments show that this extension works well.
1383241
CBC: clustering based text classification requiring minimal labeled data
Semisupervised learning methods construct classifiers using both labeled and unlabeled training data samples. While unlabeled data samples can help to improve the accuracy of trained models to certain extent, existing methods still face difficulties when labeled data is not sufficient and biased against the underlying data distribution. We present a clustering based classification (CBC) approach. Using this approach, training data, including both the labeled and unlabeled data, is first clustered with the guidance of the labeled data. Some of unlabeled data samples are then labeled based on the clusters obtained. Discriminative classifiers can subsequently be trained with the expanded labeled dataset. The effectiveness of the proposed method is justified analytically. Our experimental results demonstrated that CBC outperforms existing algorithms when the size of labeled dataset is very small.
7081422
Testing complex temporal relationships involving multiple granularities and its application to data mining (extended abstract)
A vacuum cleaner structure wherein a nozzle is mounted on a wheeled carriage, the front portion of the carriage being vertically adjustably positioned relative to the subjacent surface on which the wheels are disposed. Thus, the vacuum cleaner structure is adjustable for use with different height pile carpeting, i.e., from relatively low pile carpeting to deep shag carpeting. The wheel adjusting means includes a control member adjustably carried on the nozzle and means for adjusting the relationship of the front wheels of the carriage to the carriage as a function of adjustment of the control member. The adjustment control may include means for locking the control in different preselected positions of adjustment.
810413
Enhancing the robustness of distributed real-time middleware via end-to-end utilization control
A key challenge for distributed real-time and embedded (DRE) middleware is maintaining both system reliability and desired real-time performance in unpredictable environments where system workload and resources may fluctuate significantly. This paper presents FC-ORB, a realtime object request broker (ORB) middleware that employs end-to-end utilization control to handle fluctuations in application workload and system resources. The contributions of this paper are three-fold. First, we present a novel utilization control service that enforces desired CPU utilization bounds on multiple processors by adapting the rates of end-to-end tasks within user-specified ranges. Second, we describe a set of middleware-level mechanisms designed to support end-to-end tasks and distributed multi-processor utilization control in a real-time ORB. Finally, we present extensive experimental results on a Linux testbed. Our results demonstrate that our middleware can maintain desired utilizations in face of uncertainties and variations in task execution times, resource contentions from external workloads, and permanent processor failure. FC-ORB demonstrates that the integration of utilization control, end-to-end scheduling and fault-tolerance mechanisms in DRE middleware is a promising approach for enhancing the robustness of DRE applications in unpredictable environments
36117076
Discovering Frequent Event Patterns with Multiple Granularities in Time Sequences
An important usage of time sequences is to discover temporal patterns. The discovery process usually starts with a user specified skeleton, called an event structure, which consists of a number of variables representing events and temporal constraints among these variables; the goal of the discovery is to find temporal patterns, i.e., instantiations of the variables in the structure that appear frequently in the time sequence. The paper introduces event structures that have temporal constraints with multiple granularities, defines the pattern discovery problem with these structures, and studies effective algorithms to solve it. The basic components of the algorithms include timed automata with granularities (TAGs) and a number of heuristics. The TAGs are for testing whether a specific temporal pattern, called a candidate complex event type, appears frequently in a time sequence. Since there are often a huge number of candidate event types for a usual event structure, heuristics are presented aiming at reducing the number of candidate event types and reducing the time spent by the TAGs testing whether a candidate type does appear frequently in the sequence. These heuristics exploit the information provided by explicit and implicit temporal constraints with granularity in the given event structure. The paper also gives the results of an experiment to show the effectiveness of the heuristics on a real data set.
14477369
Markov random field modeled range image segmentation
In this paper, range image segmentation is studied in the framework of the maximum a posteriori estimation and Markov random field modeling. A novel range image segmentation model is proposed. The model serves as an evaluator for a small number of segmentation candidates obtained through a fast edge detection algorithm. A local method is employed to search for the optimal segmentation from the candidates. Experimental results show that such combination of heuristics and model-based evaluation leads to a fast and accurate segmentation.