7237530
A Sampling-Based Approach to Optimizing Top-k Queries in Sensor Networks
Wireless sensor networks generate a vast amount of data. This data, however, must be sparingly extracted to conserve energy, usually the most precious resource in battery-powered sensors. When approximation is acceptable, a model-driven approach to query processing is effective in saving energy by avoiding contacting nodes whose values can be predicted or are unlikely to be in the result set. To optimize queries such as top-k, however, reasoning directly with models of joint probability distributions can be prohibitively expensive. Instead of using models explicitly, we propose to use samples of past sensor readings. Not only are such samples simple to maintain, but they are also computationally efficient to use in query optimization. With these samples, we can formulate the problem of optimizing approximate top-k queries under an energy constraint as a linear program. We demonstrate the power and flexibility of our sampling-based approach by developing a series of topk query planning algorithms with linear programming, which are capable of efficiently producing plans with better performance and novel features. We show that our approach is both theoretically sound and practically effective on simulated and real-world datasets.
14448318
On Suspending and Resuming Dataflows
Consider a long-running, resource-intensive query Q running on a database management system (DBMS). Suppose another task T with much higher priority arrives, and we need to process T as quickly as possible and with all available resources. Ideally, the system should suspend the execution of Q, quickly release all resources held by Q, and start T using all resources. Query Q can be resumed once T finishes execution, ideally without losing any significant fraction of the work that Q had done prior to suspend. Beyond supporting mixed-priority DBMS workloads, suspend/resume of queries, or dataflows in general, is important in many other settings: Utility and grid: Dataflows now run frequently on computational utilities and grids composed of autonomous resources. When the owner of resources wants to use them, dataflows running on these resources must release control quickly, and migrate to other resources. Software rejuvenation: Benefits of software rejuvenation, the practice of rebooting enterprise computing systems regularly, are now recognized widely. Reboot is critical when performance degrades due to resource exhaustion caused by resource leaks. Suspend and resume is important in this setting because (1) the challenge of predicting completion times accurately makes it difficult to schedule task completion to match a rejuvenation schedule; (2) when performance degrades, it may not be cost-effective to wait for all dataflows to complete before rebooting.
2510058
Constraint chaining: on energy-efficient continuous monitoring in sensor networks
Wireless sensor networks have created new opportunities for data collection in a variety of scenarios, such as environmental and industrial, where we expect data to be temporally and spatially correlated. Researchers may want to continuously collect all sensor data from the network for later analysis. Suppression, both temporal and spatial, provides opportunities for reducing the energy cost of sensor data collection. We demonstrate how both types can be combined for maximal benefit. We frame the problem as one of monitoring node and edge constraints. A monitored node triggers a report if its value changes. A monitored edge triggers a report if the difference between its nodes' values changes. The set of reports collected at the base station is used to derive all node values. We fully exploit the potential of this global inference in our algorithm, CONCH, short for constraint chaining. Constraint chaining builds a network of constraints that are maintained locally, but allow a global view of values to be maintained with minimal cost. Network failure complicates the use of suppression, since either causes an absence of reports. We add enhancements to CONCH to build in redundant constraints and provide a method to interpret the resulting reports in case of uncertainty. Using simulation we experimentally evaluate CONCH's effectiveness against competing schemes in a number of interesting scenarios.
2070842
Distributed Network Querying with Bounded Approximate Caching
As networks continue to grow in size and complexity, distributed network monitoring and resource querying are becoming increasingly difficult. Our aim is to design, build, and evaluate a scalable infrastructure for answering queries over distributed measurements, at reduced costs (in terms of both network traffic and query latency) while maintaining required precision. In this infrastructure, each network node owns a set of numerical measurements and actively maintains bounds on these values cached at other nodes. We can answer queries approximately, using bounds from nearby caches to avoid contacting the owners directly. We focus on developing efficient and scalable techniques to place, locate, and manage bounded approximate caches across a large network. We have developed two approaches: One uses a recursive partitioning of the network space to place caches in a static, controlled manner, while the other uses a locality-aware distributed hash table to place caches in a dynamic and decentralized manner. In this paper, we focus on the latter approach. Experiments over a large-scale emulated network show that our techniques are very effective in reducing query costs while generating an acceptable amount of background traffic; they are also able to exploit various forms of locality that are naturally present in queries, and adapt to volatility of measurements.
214792426
TIP: a temporal extension to Informix
Commercial relational database systems today provide only limited temporal support. To address the needs of applications requiring rich temporal data and queries, we have built TIP (Temporal Information Processor), a temporal extension to the Informix database system based on its DataBlade technology. Our TIP DataBlade extends Informix with a rich set of datatypes and routines that facilitate temporal modeling and querying. TIP provides both C and Java libraries for client applications to access a TIP-enabled database, and provides end-users with a GUI interface for querying and browsing temporal data.
12872505
Value-Based Notification Conditions in Large-Scale Publish/Subscribe Systems
We address the problem of providing scalable support for subscriptions with personalized value-based notification conditions in wide-area publish/subscribe systems. Notification conditions can be fine-tuned by subscribers, allowing precise and flexible control of when events are delivered to the subscribers. For example, a user may specify that she should be notified if and only if the price of a particular stock moves outside a "radius" around her last notified value. Naive techniques for handling notification conditions are not scalable. It is challenging to share subscription processing and notification dissemination of subscriptions with personalized value-based notification conditions, because two subscriptions may see two completely different sequences of notifications even if they specify the same radius. We develop and experimentally evaluate scalable processing and dissemination techniques for these subscriptions. Our approach uses standard network substrates for notification dissemination, and avoids pushing complex application processing into the network. Compared with other alternatives, our approach generates orders of magnitude lower network traffic, and incurs lower server processing cost.
26384016
A new evolutionary approach to developing neural autonomous agents
This paper explores the use of neural networks to control robots in tasks requiring sequential and learning behavior. We propose a family competition evolutionary algorithm (FCEA) to evolve networks that can integrate these different types of behavior in a smooth and continuous manner. The approach integrates self-adaptive Gaussian mutation, self-adaptive Cauchy mutation, decreasing-based Gaussian mutation, and family competition. In order to illustrate the power of the approach, we apply this approach to two different task domains: the "artificial ant" problem and a sequential behavior problem - an agent learns to play football. From the experimental results, we find our approach performs much better than other evolutionary algorithms in these two tasks. Based on the results from our experiments, it is shown that our approach can evolve neural networks to provide a means of integrating, sequencing and learning within a single control system.
14693913
Feature Subset Selection Using a Genetic Algorithm
Practical pattern-classification and knowledge-discovery problems require the selection of a subset of attributes or features to represent the patterns to be classified. The authors' approach uses a genetic algorithm to select such subsets, achieving multicriteria optimization in terms of generalization accuracy and costs associated with the features.
342298
Nonlinear information aggregation via evidential reasoning in multiattribute decision analysis under uncertainty
In many decision situations, it is inevitable to deal with both quantitative and qualitative information under uncertainty. Evidence-based reasoning within a multiple criteria decision analysis framework provides an alternative way of handling such information systematically and consistently. In this paper, the evidential reasoning (ER) approach is introduced, which is based on a recursive ER algorithm that, in essence, constitutes a nonlinear information aggregation process. To facilitate the application of the ER approach and as an indispensable part of its development, the nonlinear features of the ER information aggregation process need to be thoroughly investigated and properly understood. This forms the theme of this paper where the nonlinear features are explored by examining typical reasoning patterns in aggregating harmonic, quasi-harmonic, and contradictory decision information. This analytical investigation provides insights into the recursive nature of the ER approach as well as valuable experience that could be useful to other researchers and practitioners interested in developing and applying operation research/artificial intelligence (OR/AI)-based approaches for decision analysis under uncertainty. The analytical study is complemented by the numerical studies of two application examples. The analysis of a quality assessment problem for motor engines is aimed to show the step-by-step process of implementing the ER approach and to illustrate its nonlinear features in a real-life decision situation. The study of a more complex assessment problem in ship design is intended to demonstrate the potential of the ER approach and its supporting software for dealing with general decision problems.
17739128
Modified Hough Transforms for Object Feature Extraction
In this paper, we propose the use of modified Hough transforms to efficiently extract object feature parameters, which are usually contaminated by heavily noisy corrugation and discontinuity. The modified HT (MHT) is developed by introducing spatial and parameter weighting functions to improve the detection performance for the traditional Hough transform (HT), which generally fails to robustly detect natural object parameters. Using designed test patterns and real images, simulations show that the proposed weighting functions are helpful in detecting noise-corrupted object features. Due to its robustness, the MHT can be easily figured with a coarse-to-fine adaptive search mechanism to reduce the huge amount of computation for feature parameters extraction.
20784712
A fault-tolerant distributed channel allocation scheme for cellular networks
In cellular networks, it is vital to allocate communication channels efficiently because the bandwidth allocated for cellular communication is limited. If channels are statically allocated, as is the case in current cellular networks, a cell may run out of channels when a large number of mobile hosts move to the cell. To overcome this problem, dynamic channel allocation approaches have been proposed. Under dynamic channel allocation, channels are allocated to cells on demand, thus increasing channel utilization. Such channel allocation approaches fall under two categories, namely, centralized and distributed. Centralized approaches are neither scalable nor reliable, while distributed approaches have the potential to be both reliable and scalable. In the distributed approaches, a mobile service station is responsible for allocating channels to mobile hosts in the same cell. In cellular networks, mobile service stations may fail. In addition, the network may encounter intermittent network congestion and/or link failures. It is desirable for a channel allocation algorithm to work well, even in the presence of network congestion, link failures, and/or mobile service station failures. In this paper, we present a distributed dynamic channel allocation scheme for cellular networks that is fault-tolerant. Our approach can tolerate the failure of mobile service stations, link failure, and network congestion, and make efficient reuse of channels. We also provide results of the performance evaluation of our algorithm.