Susan V. Vrbsky

APPROXIMATE-a query processor that produces monotonically improving approximate answers

APPROXIMATE, a query processor that makes approximate answers available if part of the database is unavailable, or if there is not enough time to produce an exact answer, is described. The processor implements approximate query processing, and the accuracy of the approximate result produced improves monotonically with the amount of data retrieved to produce the result. The monotone query processing algorithm of APPROXIMATE works within a standard relational algebra framework. APPROXIMATE maintains semantic information for approximate query processing at an underlying level, and can be implemented on a relational database system with little change to the relational architecture. It is shown how APPROXIMATE is implemented to make effective use of the semantic support. The additional overhead required by APPROXIMATE is described. >

An on-line replication strategy to increase availability in Data Grids

Data is typically replicated in a Data Grid to improve the job response time and data availability. Strategies for data replication in a Data Grid have previously been proposed, but they typically assume unlimited storage for replicas. In this paper, we address the system-wide data availability problem assuming limited replica storage. We describe two new metrics to evaluate the reliability of the system, and propose an on-line optimizer algorithm that can Minimize the Data Missing Rate (MinDmr) in order to maximize the data availability. Based on MinDmr, we develop four optimizers associated with four different file access prediction functions. Simulation results utilizing the OptorSim show our MinDmr strategies achieve better performance overall than other strategies in terms of the goal of data availability using the two new metrics.

An object-oriented query processor that produces monotonically improving approximate answers

An object-oriented query processor is described that makes approximate answers available if there is not enough time to product an exact answer or if part of the database is unavailable. The accuracy of the approximate result produced improves monotonically with the amount of data retrieved to produce the result. The query-processing algorithm is based on an approximate relational data model and works within a standard relational algebra framework. The query processor maintains an object-oriented view on an underlying level and can be implemented on a relational database system with little change to the relational architecture. It is shown how a monotone query-processing strategy can be implemented, making effective use of semantic information presented by the object-oriented view.<<ETX>>

A pull-based broadcast algorithm that considers timing constraints

There are many situations in which we need to incorporate real-time constraints in broadcasting systems for mobile environments. In this paper, we study broadcast scheduling strategies for pull-based broadcast with timing constraints in the form of deadlines. Unlike previously proposed scheduling algorithms for broadcast systems which aim to minimize the mean access time, our goal is to identify scheduling algorithms for broadcast systems that ensure requests meet their deadlines. We present a detailed study of the performance of traditional nonmobile real-time strategies and non-real-time mobile strategies, and demonstrate that traditional real-time algorithms do not always perform the best in a mobile environment. We propose a model of a pull-based real-time broadcast system and also propose an efficient scheduling algorithm, called Aggregated Critical Requests (ACR), which is designed for timely delivery of data to mobile clients.

Triggered Updates for Temporal Consistency in Real-TimeDatabases

A real-time database systemhas temporal consistency constraints in addition to timing constraints.The timing constraints require a transaction to be completedby a specified deadline, and the temporal consistency constraintsrequire that temporal data read by a transaction be up-to-date.If a transaction reads out-of-date data, it will become temporallyinconsistent. A real-time database system consists of differenttypes of temporal data objects, including derived objects. Thevalue of a derived object is computed from a set of other objects,known as the read-set of the derived object. The derived objectmay not always reflect the current state of its read-set; a derivedobject can become out-of-date even if its read-set is up-to-date.Any subsequent transaction reading the derived object will thenbecome temporally inconsistent. In this case, in order to readup-to-date objects, a transaction will have to wait until someother transaction updates the out-of-date object. However, indoing so, the waiting transaction may miss its deadline, particularlyif the update is not periodic but instead arrives randomly. Wepropose to update the outdated objects so that not only is thetemporal consistency improved, but also the number of misseddeadlines does not increase significantly, and as a result thereis an overall improvement in the performance of the system. Wepropose, implement and study a novel approach, to be known astriggered updates, to improve temporal consistency in firm real-timedatabase systems when updates are not periodic. We identify propertiesof triggered updates and explain how they work by giving bothan intuitive and a probabilistic analysis. We present strategiesfor generating triggered updates, discuss their suitability invarious contexts and perform a detailed simulation study to evaluatetheir performance. Results show that it is possible to improvetemporal consistency without degrading the timeliness of real-time database systems to a great deal.

Data Replication and Power Consumption in Data Grids

While data grids can provide the ability to solve large-scale applications which require the processing of large amounts of data, they have been recognized as extremely energy inefficient. Computing elements can be located far away from the data storage elements. A common solution to improve availability and file access time in such environments is to replicate the data, resulting in the creation of copies of data files at many different sites. The energy efficiency of the data centers storing this data is one of the biggest issues in data intensive computing. Since power is needed to transmit, store and cool the data, we propose to minimize the amount of data transmitted and stored by utilizing smart replication strategies that are data aware. In this paper we present a new data replication approach, called the sliding window replica strategy (SWIN), that is not only data aware, but is also energy efficient. We measure the performance of SWIN and existing replica strategies on our Sage green cluster to study the power consumption of the strategies. Results from this study have implications beyond our cluster to the management of data in clouds.

Maintaining security in firm real-time database systems

Many real-time database systems, such as military institutions and government agencies, are contained in environments that exhibit restricted access of information, where mandatory access control for security is required. Hence, in addition to timing constraints, real-time database systems have security constraints. Conventional multi-level secure database models are inadequate for time-critical applications and conventional real-time database models do not support security constraints. The objective of this work is to incorporate security constraints in real-time database systems in such a way that not only is security achieved, but achieving security does not degrade real-time performance significantly in terms of deadlines missed. We propose a new optimistic concurrency control algorithm for secure firm real-time databases. Results show that the algorithm performs fairly well in terms of security and timeliness compared to a non-secure algorithm. We argue and show that achieving more security does not necessarily mean more sacrifice in real-time performance.

A data model for approximate query processing of real-time databases

Abstract A real-time system has specific time constraints for the processing of a transaction as well as temporal consistency constraints for its temporal data. If it is not possible to produce an exact answer to a database query within the specified time constraints, for many applications it may be better to produce an approximate answer than to produce no answer at all or to wait for an exact answer and miss a deadline. Approximate query processing can be used to provide approximate answers to database queries for such applications. However, approximate query processing does not address the time dimension of the data. In this paper we extend the theoretical basis of approximate query processing to include the temporal dimension of real-time databases and present a temporal data model for approximate query processing. We describe an approximation that is designed to include the temporal data and address the temporal consistency constraints of a real-time database. We present monotone approximate relational algebra operations that are redefined to include the temporal dimension of the data. We also describe the semantic support of an implementation of an approximate query processor for temporal data that is based on this data model.

CARE: an automobile crash data analysis tool

The Critical Analysis Reporting Environment provides an efficient tool for transportation safer engineers and policymakers to use in analyzing the categorical crash data typically obtained from police reports. CAPE has proven successful in the traffic safety community for two reasons: its simplicity and its efficiency. It is currently being used in several states.

Building a wireless capturing tool for WiFi

WiFi is becoming increasingly prevalent nowadays, whether as a simple range extender for a home wired Ethernet interface or a wireless deployment throughout an enterprise. Wireless local area networks (WLANs) provide us with mobility, convenience, and low cost. At the same time, WiFi is unsafe and is more vulnerable than traditional Ethernet, so that anyone familiar with wireless networks can initiate an attack. One strategy to identify potentially malicious unauthorized users is through packet capturing. Although there are several software products available for packet capture, there is currently no paper in the literature that describes how to build a software tool to capture WiFi frames and its associated functions. In this paper, we present how we build our frame capture tool along with a set of implementation techniques for automatically capturing all the frames and analyzing an attack on a WiFi. In our research, we focus on the WiFi medium access control (MAC) layer for wireless network analysis. We also discuss what we learned and the limitations which we discovered when implementing the tool. Copyright