949732
Optimizing Queries Across Diverse Data Sources
Businessestoday need to interrelate data stored in diverse systems with differing capabilities, ideally via a single high-level query interface. We present the design of a query optimizer for Garlic [C 95], a middleware system designedto integrate data from a broad range of data sources with very different query capabilities. Garlic’s optimizer extends the rule-based approach of [Loh88] to work in a heterogeneous environment, by defining generic rules for the middleware and using wrapper-provided rules to encapsulate the capabilities of each data source. This approach offers great advantages in terms of plan quality, extensibility to new sources, incremental implementationof rules for new sources, and the ability to express the capabilities of a diverse set of sources. We describe the design and implementationof this optimizer, and illustrate its actions through an example.
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.
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.
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.
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.
46223238
Environmental impact assessment using the evidential reasoning approach
Environmental impact assessment (EIA) problems are often characterised by a large number of identified environmental factors that are qualitative in nature and can only be assessed on the basis of human judgments, which inevitably involve various types of uncertainties such as ignorance and fuzziness. So, EIA problems need to be modelled and analysed using methods that can handle uncertainties. The evidential reasoning (ER) approach provides such a modelling framework and analysis method. In this paper the ER approach will be applied to conduct EIA analysis for the first time. The environmental impact consequences are characterized by a set of assessment grades that are assumed to be collectively exhaustive and mutually exclusive. All assessment information, quantitative or qualitative, complete or incomplete, and precise or imprecise, is modelled using a unified framework of a belief structure. The original ER approach with a recursive ER algorithm will be introduced and a new analytical ER algorithm will be investigated which provides a means for using the ER approach in decision situations where an explicit ER aggregation function is needed such as in optimisation problems. The ER approach will be used to aggregate multiple environmental factors, resulting in an aggregated distributed assessment for each alternative policy. A numerical example and its modified version are studied to illustrate the detailed implementation process of the ER approach and demonstrate its potential applications in EIA.
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.
5708746
On the Robust Single Machine Scheduling Problem
The single machine scheduling problem with sum of completion times criterion (SS) can be solved easily by the Shortest Processing Time (SPT) rule. In the case of significant uncertainty of the processing times, a robustness approach is appropriate. In this paper, we show that the robust version of the (SS) problem is NP-complete even for very restricted cases. We present an algorithm for finding optimal solutions for the robust (SS) problem using dynamic programming. We also provide two polynomial time heuristics and demonstrate their effectiveness.
11796157
Functional Brain Image Analysis Using Joint Function-Structure Priors
We propose a new method for context-driven analysis of functional magnetic resonance images (fMRI) that incorporates spatial relationships between functional parameter clusters and anatomical structure directly for the first time. We design a parametric scheme that relates functional and structural spatially-compact regions in a single unified manner. Our method is motivated by the fact that the fMRI and anatomical MRI (aMRI) have consistent relations that provide configurations and context that aid in fMRI analysis. We develop a statistical decision-making strategy to estimate new fMRI parameter images (based on a General Linear Model-GLM) and spatially-clustered zones within these images. The analysis is based on the time-series data and contextual information related to appropriate spatial grouping of parameters in the functional data and the relationship of this grouping to relevant gray matter structure from the anatomical data. We introduce a representation for the joint prior of the functional and structural information, and define a joint probability distribution over the variations of functional clusters and the related structure contained in a set of training images. We estimate the Maximum A Posteriori (MAP) functional parameters, formulating the function-structure model in terms of level set functions. Results from 3D fMRI and aMRI show that this context-driven analysis potentially extracts more meaningful information than the standard GLM approach.
252049
MECA: an extensible, expressive system and language for statically checking security properties
This paper describes a system and annotation language, MECA, for checking security rules. MECA is expressive and designed for checking real systems. It provides a variety of practical constructs to effectively annotate large bodies of code. For example, it allows programmers to write programmatic annotators that automatically annotate large bodies of source code. As another example, it lets programmers use general predicates to determine if an annotation is applied; we have used this ability to easily handle kernel backdoors and other false-positive inducing constructs. Once code is annotated, MECA propagates annotations aggressively, allowing a single manual annotation to derive many additional annotations (e.g., over one hundred in our experiments) freeing programmers from the heavy manual effort required by most past systems.MECA is effective. Our most thorough case study was a user-pointer checker that used 75 annotations to check thousands of declarations in millions of lines of code in the Linux system. It found over forty errors, many of which were serious, while only having eight false positives.