12547451
Data-Driven Processing in Sensor Networks
Wireless sensor networks are poised to enable continuous data collection on unprecedented scales, in terms of area location and size, and frequency. This is a great boon to elds such as ecological modeling. We are collaborating with researchers to build sophisticated temporal and spatial models of forest growth, utilizing a variety of measurements. There exists a crucial challenge in supporting this activity: network nodes have limited battery life, and radio communication is the dominant energy consumer. The straightforward solution of instructing all nodes to report their measurements as they are taken to a base station will quickly consume the network’s energy. On the other hand, the solution of building models for node behavior and substituting these in place of the actual measurements is in conict with the end goal of constructing models. To address this dilemma, we propose data-driven processing, the goal of which is to provide continuous data without continuous reporting, but with checks against the actual data. Our primary strategy for this is suppression, which uses in-network monitoring to limit the amount of communication to the base station. Suppression employs models for optimization of data collection, but not at the risk of correctness. We discuss techniques for designing data-driven collection, such as building suppression schemes and incorporating models into them. We then present and address some of the major challenges to making this approach practical, such as handling failure and avoiding the need to co-design the network application and communication layers.
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.
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.
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.
13993519
Interval efficiency assessment using data envelopment analysis
This paper studies how to conduct efficiency assessment using data envelopment analysis (DEA) in interval and/or fuzzy input-output environments. A new pair of interval DEA models is constructed on the basis of interval arithmetic, which differs from the existing DEA models handling interval data in that the former is a linear CCR model without the need of extra variable alternations and uses a fixed and unified production frontier (i.e. the same constraint set) to measure the efficiencies of decision-making units (DMUs) with interval input and output data, while the latter is usually a nonlinear optimization problem with the need of extra variable alternations or scale transformations and utilizes variable production frontiers (i.e. different constraint sets) to measure interval efficiencies. Ordinal preference information and fuzzy data are converted into interval data through the estimation of permissible intervals and @a-level sets, respectively, and are incorporated into the interval DEA models. The proposed interval DEA models are developed for measuring the lower and upper bounds of the best relative efficiency of each DMU with interval input and output data, which are different from the interval formed by the worst and the best relative efficiencies of each DMU. A minimax regret-based approach (MRA) is introduced to compare and rank the efficiency intervals of DMUs. Two numerical examples are provided to show the applications of the proposed interval DEA models and the preference ranking approach.
11069995
Power efficient encoding techniques for off-chip data buses
Reducing the power consumption of computing devices has gained a lot of attention recently. Many research works have focused on reducing power consumption in the off-chip buses as they consume a significant amount of total power. Since the bus power consumption is proportional to the switching activity, reducing the bus switching is an effective way to reduce bus power. While numerous techniques exist for reducing bus power in address buses, only a handful of techniques have been proposed for data-bus power reduction, where Frequent Value Encoding (FVE) is the best existing scheme to reduce the transition activity on the data buses.In this paper, we propose improved frequent value data-bus encoding techniques aimed at reducing more switching activity and hence, more power consumption. We propose three new schemes and five new variations to exploit bit-wise temporal and spatial locality in the data bus values. Our technique does not use additional external control signal and captures bit-wise locality to efficiently encode data values. For all the embedded and SPEC applications we tested, the overall average switching reduction is 53% over unencoded data and 11% more than the conventional FVE scheme.
8365397
Input modeling when simple models fail
A simulation model is composed of inputs and logic; the inputs represent the uncertainty or randomness in the system, while the logic determines how the system reacts to the uncertain elements. Simple input models, consisting of independent and identically distributed sequences of random variates from standard probability distributions, are included in every commercial simulation language. Software to fit these distributions to data is also available. In this tutorial we describe input models that are useful when simple models are not.
1590392
Inference and learning methodology of belief-rule-based expert system for pipeline leak detection
Belief rule based expert systems are an extension of traditional rule based systems and are capable of representing more complicated causal relationships using different types of information with uncertainties. This paper describes how the belief rule based expert systems can be trained and used for pipeline leak detection. Pipeline operations under different conditions are modelled by a belief rule base using expert knowledge, which is then trained and fine tuned using pipeline operating data, and validated by testing data. All training and testing data are collected and scaled from a real pipeline. The study demonstrates that the belief rule based system is flexible, can be adapted to represent complicated expert systems, and is a valid novel approach for pipeline leak detection.
36888588
Efficient connected-index finite-length arithmetic codes
In this paper, we propose a connected-index approach to construct efficient finite-length arithmetic codes by splitting the information of the last nonfitting symbol into the current and subsequent codewords. The proposed arithmetic codes, which limit the error propagation in about one block, require neither a post-appended end-of-block symbol, nor pre-affixed side-information, to characterize the number of encoded symbols. Hence, the proposed finite-length arithmetic codes can nearly achieve the coding efficiency attained by infinite-length arithmetic codes. With high coding efficiency, limited error-propagation, and the regular process, the proposed coding approach is suitable for information exchange with small packets in modern high-speed network systems.