15083897
Grouping web image search result
In this paper, we propose a Web image search result organizing method to facilitate user browsing. We formalize this problem as a salient image region pattern extraction problem. Given the images returned by Web search engine, we first segment the images into homogeneous regions and quantize the environmental regions into image codewords. The salient codeword "phrases" are then extracted and ranked based on a regression model learned from human labeled training data. According to the salient "phrases", images are assigned to different clusters, with the one nearest to the centroid as the entry for the corresponding cluster. Satisfying experimental results show the effectiveness of our proposed method.
32712892
Learning Image Manifold Using Web Data
Manifold learning has become a hot research topic in recent years and is widely used in the area of dimension reduction, information retrieval and ranking, etc. However, how to reconstruct the intrinsic manifold from the observed data points, i.e. what is the proper data point distance measure, is still an open problem. In this paper, we propose to take advantages of the information provided by web-pages and the image-related website link structure to learn the Web image manifold, which better approaches to the intrinsic manifold than those learned by previous methods which use Euclidean alike distances to construct the initial affinity matrix. Experimental results prove the effectiveness of our learned Web image manifold.
2235439
Learn from web search logs to organize search results
Effective organization of search results is critical for improving the utility of any search engine. Clustering search results is an effective way to organize search results, which allows a user to navigate into relevant documents quickly. However, two deficiencies of this approach make it not always work well: (1) the clusters discovered do not necessarily correspond to the interesting aspects of a topic from the user's perspective; and (2) the cluster labels generated are not informative enough to allow a user to identify the right cluster. In this paper, we propose to address these two deficiencies by (1) learning "interesting aspects" of a topic from Web search logs and organizing search results accordingly; and (2) generating more meaningful cluster labels using past query words entered by users. We evaluate our proposed method on a commercial search engine log data. Compared with the traditional methods of clustering search results, our method can give better result organization and more meaningful labels.
5829649
Information technology implementation for a distributed data system serving Earth scientists: seasonal to interannual ESIP
We address the implementation of a distributed data system designed to serve Earth system scientists. A consortium led by George Mason University has been funded by NASA's Working Prototype Earth Science Information Partner (WP-ESIP) program to develop, implement, and operate a distributed data and information system. The system will address the research needs of seasonal to interannual scientists whose research focus includes phenomena such as El Nino, monsoons and associated climate studies. The system implementation involves several institutions using a multitiered client-server architecture. Specifically the consortium involves an information system of three physical sites, GMU, the Center for Ocean-Land-Atmosphere Studies (COLA) and the Goddard Distributed Active Archive Center, distributing tasks in the areas of user services, access to data, archiving, and other aspects enabled by a low-cost, scalable information technology implementation. The project can serve as a model for a larger WP-ESIP Federation to assist in the overall data information system associated with future large Earth Observing System data sets and their distribution. The consortium has developed innovative information technology techniques such as content based browsing, data mining and associated component working prototypes; analysis tools particularly GrADS developed by COLA, the preferred analysis tool of the working seasonal to interannual communities; and a Java front-end query engine working prototype.
16502768
Improving query I/O performance by permuting and refining block request sequences
The I/O performance of query processing can be improved using two complementary approaches: improve the buffer and the file system management policies of the DB buffer manager and the OS file system manager (e.g. page replacement), or improve the sequence of requests that are submitted to a file system manager and that lead to actual I/O's (block request sequences). This paper takes the latter approach. Exploiting common file system practices as found in Linux, we propose four techniques for permuting and refining block request sequences: Block-Level I/O Grouping, File-Level I/O Grouping, I/O Ordering, and Block Recycling. To manifest these techniques, we create two new plan operations, MMS and SHJ, each of which adopts some of the block request refinement techniques above. We implement the new plan operations on top of Postgres running on Linux, and show experimental results that demonstrate up to a factor of 4 performance benefit from the use of these techniques.
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.
631362
DEUCON: Decentralized End-to-End Utilization Control for Distributed Real-Time Systems
Many real-time systems must control their CPU utilizations in order to meet end-to-end deadlines and prevent overload. Utilization control is particularly challenging in distributed real-time systems with highly unpredictable workloads and a large number of end-to-end tasks and processors. This paper presents the decentralized end-to-end utilization control (DEUCON) algorithm, which can dynamically enforce the desired utilizations on multiple processors in such systems. In contrast to centralized control schemes adopted in earlier works, DEUCON features a novel decentralized control structure that requires only localized coordination among neighbor processors. DEUCON is systematically designed based on advances in distributed model predictive control theory. Both control-theoretic analysis and simulations show that DEUCON can provide robust utilization guarantees and maintain global system stability despite severe variations in task execution times. Furthermore, DEUCON can effectively distribute the computation and communication cost to different processors and tolerate considerable communication delay between local controllers. Our results indicate that DEUCON can provide a scalable and robust utilization control for large-scale distributed real-time systems executing in unpredictable environments.