Mohamed A. Sharaf

TiNA: a scheme for temporal coherency-aware in-network aggregation

This paper presents TiNA, a scheme for minimizing energy consumption in sensor networks by exploiting end-user tolerance to temporal coherency. TiNA utilizes temporal coherency tolerances to both reduce the amount of information transmitted by individual nodes (communication cost dominates power usage in sensor networks), and to improve quality of data when not all sensor readings can be propagated up the network within a given time constraint. TiNA was evaluated against a traditional in-network aggregation scheme with respect to power savings as well as the quality of data for aggregate queries. Preliminary results show that TiNA can reduce power consumption by up to 50% without any loss in the quality of data.

Algorithms and metrics for processing multiple heterogeneous continuous queries

The emergence of monitoring applications has precipitated the need for Data Stream Management Systems (DSMSs), which constantly monitor incoming data feeds (through registered continuous queries), in order to detect events of interest. In this article, we examine the problem of how to schedule multiple Continuous Queries (CQs) in a DSMS to optimize different Quality of Service (QoS) metrics. We show that, unlike traditional online systems, scheduling policies in DSMSs that optimize for average response time will be different from policies that optimize for average slowdown, which is a more appropriate metric to use in the presence of a heterogeneous workload. Towards this, we propose policies to optimize for the average-case performance for both metrics. Additionally, we propose a hybrid scheduling policy that strikes a fine balance between performance and fairness, by looking at both the average- and worst-case performance, for both metrics. We also show how our policies can be adaptive enough to handle the inherent dynamic nature of monitoring applications. Furthermore, we discuss how our policies can be efficiently implemented and extended to exploit sharing in optimized multi-query plans and multi-stream CQs. Finally, we experimentally show using real data that our policies consistently outperform currently used ones.

Emerging event detection in social networks with location sensitivity

With the increasing number of real-world events that are originated and discussed over social networks, event detection is becoming a compelling research issue. However, the traditional approaches to event detection on large text streams are not designed to deal with a large number of short and noisy messages. This paper proposes an approach for the early detection of emerging hotspot events in social networks with location sensitivity. We consider the message-mentioned locations for identifying the locations of events. In our approach, we identify strong correlations between user locations and event locations in detecting the emerging events. We evaluate our approach based on a real-world Twitter dataset. Our experiments show that the proposed approach can effectively detect emerging events with respect to user locations that have different granularities.

On-demand data broadcasting for mobile decision making

The wide spread of mobile computing devices is transforming the newly emerged e-business world into a mobile e-business one, a world in which hand-held computers are the users front-ends to access enterprise data. For good mobile decision making, users need to count on up-to-date, business-critical data. Such data are typically in the form of summarized information tailored to suit the users analysis interests. In this paper, we are addressing the issue of time and energy efficient delivery of summary tables to mobile users with hand-held computers equipped with OLAP (On-Line Analytical Processing) front-end tools. Towards this, we propose a new on-demand scheduling algorithm, called STOBS, that exploits the derivation semantics among OLAP summary tables. It maximizes the aggregated data sharing between mobile users and reduces the broadcast length for satisfying a set of requests compared to the already existing techniques. The algorithm effectiveness with respect to access time and energy consumption is evaluated using simulation.

Preemptive rate-based operator scheduling in a data stream management system

Summary form only given. Data stream management systems are being developed to process continuous queries over multiple data streams. These continuous queries are typically used for monitoring purposes where the detection of an event might trigger a sequence of actions or the execution of a set of specified tasks. Such events are identified by tuples produced by a query and hence, it is important to produce the available portions of a query result as early as possible. A core element for improving the interactive performance of a continuous query is the operator scheduler. An operator scheduler is particularly important when the processing requirements and the productivity of different streams are highly skewed. The need for an operator scheduler becomes even more crucial when tuples from different streams arrive asynchronously. To meet these needs, we are proposing a preemptive rate-based scheduling policy that handles the asynchronous nature of tuple arrival and the heterogeneity in the query plan. Experimental results show the significant improvements provided by our proposed policy.

Dynamic partitioning of the cache hierarchy in shared data centers

Due to the imperative need to reduce the management costs of large data centers, operators multiplex several concurrent database applications on a server farm connected to shared network attached storage. Determining and enforcing per-application resource quotas in the resulting cache hierarchy, on the fly, poses a complex resource allocation problem spanning the database server and the storage server tiers. This problem is further complicated by the need to provide strict Quality of Service (QoS) guarantees to hosted applications. In this paper, we design and implement a novel coordinated partitioning technique of the database buffer pool and storage cache between applications for any given cache replacement policy and per-application access pattern. We use statistical regression to dynamically determine the mapping between cache quota settings and the resulting per-application QoS. A resource controller embedded within the database engine actuates the partitioning of the two-level cache, converging towards the configuration with maximum application utility, expressed as the service provider revenue in that configuration, based on a set of latency sample points. Our experimental evaluation, using the MySQL database engine, a server farm with consolidated storage, and two e-commerce benchmarks, shows the effectiveness of our technique in enforcing application QoS, as well as maximizing the revenue of the service provider in shared server farms.

Predicting Elections from Social Networks Based on Sub-event Detection and Sentiment Analysis

Social networks are widely used by all kinds of people to express their opinions. Predicting election outcomes is now becoming a compelling research issue. People express themselves spontaneously with respect to the social events in their social networks. Real time prediction on ongoing election events can provide feedback and trend analysis for politicians and news analysts to make informed decisions. This paper proposes an approach to predicting election results by incorporating sub-event detection and sentiment analysis in social networks to analyse as well as visualise political preferences revealed by those social network users. Extensive experiments are conducted to evaluate the performance of our approach based on a real-world Twitter dataset. Our experiments show that the proposed approach can effectively predict the election results over the given baselines.

Three-Level Processing of Multiple Aggregate Continuous Queries

Aggregate Continuous Queries (ACQs) are both a very popular class of Continuous Queries (CQs) and also have a potentially high execution cost. As such, optimizing the processing of ACQs is imperative for Data Stream Management Systems (DSMSs) to reach their full potential in supporting (critical) monitoring applications. For multiple ACQs that vary in window specifications and pre-aggregation filters, existing multiple ACQs optimization schemes assume a processing model where each ACQ is computed as a final-aggregation of a sub-aggregation. In this paper, we propose a novel processing model for ACQs, called Tri Ops, with the goal of minimizing the repetition of operator execution at the sub-aggregation level. We also propose Tri Weave, a Tri Ops-aware multi-query optimizer. We analytically and experimentally demonstrate the performance gains of our proposed schemes which shows their superiority over alternative schemes. Finally, we generalize Tri Weave to incorporate the classical subsumption-based multi-query optimization techniques.

DivIDE: efficient diversification for interactive data exploration

Today, Interactive Data Exploration (IDE) has become a main constituent of many discovery-oriented applications, in which users repeatedly submit exploratory queries to identify interesting subspaces in large data sets. Returning relevant yet diverse results to such queries provides users with quick insights into a rather large data space. Meanwhile, search results diversification adds additional cost to an already computationally expensive exploration process. To address this challenge, in this paper, we propose a novel diversification scheme called DivIDE, which targets the problem of efficiently diversifying the results of queries posed during data exploration sessions. In particular, our scheme exploits the properties of data diversification functions while leveraging the natural overlap occurring between the results of different queries so that to provide significant reductions in processing costs. Our extensive experimental evaluation on both synthetic and real data sets shows the significant benefits provided by our scheme as compared to existing methods.

A web-based approach to data imputation

In this paper, we present WebPut, a prototype system that adopts a novel web-based approach to the data imputation problem. Towards this, Webput utilizes the available information in an incomplete database in conjunction with the data consistency principle. Moreover, WebPut extends effective Information Extraction (IE) methods for the purpose of formulating web search queries that are capable of effectively retrieving missing values with high accuracy. WebPut employs a confidence-based scheme that efficiently leverages our suite of data imputation queries to automatically select the most effective imputation query for each missing value. A greedy iterative algorithm is proposed to schedule the imputation order of the different missing values in a database, and in turn the issuing of their corresponding imputation queries, for improving the accuracy and efficiency of WebPut. Moreover, several optimization techniques are also proposed to reduce the cost of estimating the confidence of imputation queries at both the tuple-level and the database-level. Experiments based on several real-world data collections demonstrate not only the effectiveness of WebPut compared to existing approaches, but also the efficiency of our proposed algorithms and optimization techniques.