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.
6284709
Satisfiability of Quantitative Temporal Constraints with Multiple Granularities
Most work on temporal constraints has ignored the subtleties involved in dealing with multiple time granularities. This paper considers a constraint satisfaction problem (CSP) where binary quantitative constraints in terms of different time granularities can be specified on a set of variables, and unary constraints are allowed to limit the domain of variables. Such a CSP cannot be trivially reduced to one of the known CSP problems. The main result of the paper is a complete algorithm for checking consistency and finding a solution. The complexity of the algorithm is studied in the paper under different assumptions about the granularities involved in the CSP, and a second algorithm is proposed to improve the efficiency of the backtracking process needed to obtain all the solutions of the CSP. 
 
The work of Wang and Jajodia was partially supported by the National Science Foundation under the grant IRI-9633541.
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.
34417323
Specification Methodology: An Integrated Relational Approach
The effectiveness of set theoretic concepts for the purpose of software specification is becoming more and more widely recognized. This paper presents an integrated relational methodology for software specification, and shows its effectivness when applied to a practical example.
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.
14053941
AnnoSearch: Image Auto-Annotation by Search
Although it has been studied for several years by computer vision and machine learning communities, image annotation is still far from practical. In this paper, we present AnnoSearch, a novel way to annotate images using search and data mining technologies. Leveraging the Web-scale images, we solve this problem in two-steps: 1) searching for semantically and visually similar images on the Web, 2) and mining annotations from them. Firstly, at least one accurate keyword is required to enable text-based search for a set of semantically similar images. Then content-based search is performed on this set to retrieve visually similar images. At last, annotations are mined from the descriptions (titles, URLs and surrounding texts) of these images. It worth highlighting that to ensure the efficiency, high dimensional visual features are mapped to hash codes which significantly speed up the content-based search process. Our proposed approach enables annotating with unlimited vocabulary, which is impossible for all existing approaches. Experimental results on real web images show the effectiveness and efficiency of the proposed algorithm.
8744802
On static and dynamic partitioning behavior of large-scale networks
In this paper, we analyze the problem of network disconnection in the context of large-scale P2P networks and understand how both static and dynamic patterns of node failure affect the resilience of such graphs. We start by applying classical results from random graph theory to show that a large variety of deterministic and random P2P graphs almost surely (i.e., with probability 1-o(1)) remain connected under random failure if and only if they have no isolated nodes. This simple, yet powerful, result subsequently allows us to derive in closed-form the probability that a P2P network develops isolated nodes, and therefore partitions, under both types of node failure. We finish the paper by demonstrating that our models match simulations very well and that dynamic P2P systems are extremely resilient under node churn as long as the neighbor replacement delay is much smaller than the average user lifetime
1347401
Decentralized utilization control in 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 that can dynamically enforce desired utilizations on multiple processors in such systems. In contrast to centralized control schemes adopted in earlier work, DEUCON features a novel decentralized control structure that only requires localized coordination among neighbor processors. DEUCON is systematically designed based on recent 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 scalable and robust utilization control for large-scale distributed real-time systems executing in unpredictable environments
1902919
Multi-Conditional Learning: Generative/Discriminative Training for Clustering and Classification
This paper presents multi-conditional learning (MCL), a training criterion based on a product of multiple conditional likelihoods. When combining the traditional conditional probability of "label given input" with a generative probability of "input given label" the later acts as a surprisingly effective rerularizer. When applied to models with latent variables, MCL combines the structure-discovery capabilities of generative topic models, such as latent Dirichlet allocation and the exponential family harmonium, with the accuracy and robustness of discriminative classifiers, such as logistic regression and conditional random fields. We present results on several standard text data sets showing significant reductions in classification error due to MCL regularization, and substantial gains in precision and recall due to the latent structure discovered under MCL.