Hicham G. Elmongui

Secure supply-chain protocols

Supply chain interactions have huge economic importance, yet these interactions are managed inefficiently. One of the major sources of inefficiency in supply-chain management is information asymmetry; i.e., information that is available to one or more organizations in the chain (e.g., manufacturer, retailer) is not available to others. There are several causes of information asymmetry, among them fear that a powerful buyer or supplier will take advantage of private information, that information will leak to a competitor, etc. We propose secure supply-chain collaboration (SSCC) protocols that enable supply-chain partners to cooperatively achieve desired system-wide goals without revealing the private information of any of the parties, even though the jointly computed decisions require the information of all the parties. Secure supply-chain collaboration has the potential to improve supply-chain management practice, and by removing a major inefficiency therein, improves productivity. We present specific SSCC protocols for two types of supply-chain interactions: capacity allocation, and e-auctions (electronic auctions). In the course of doing so, we design techniques that are of independent interest, and are likely to be useful in the design of future SSCC protocols.

Adaptive rank-aware query optimization in relational databases

Rank-aware query processing has emerged as a key requirement in modern applications. In these applications, efficient and adaptive evaluation of top-k queries is an integral part of the application semantics. In this article, we introduce a rank-aware query optimization framework that fully integrates rank-join operators into relational query engines. The framework is based on extending the System R dynamic programming algorithm in both enumeration and pruning. We define ranking as an interesting physical property that triggers the generation of rank-aware query plans. Unlike traditional join operators, optimizing for rank-join operators depends on estimating the input cardinality of these operators. We introduce a probabilistic model for estimating the input cardinality, and hence the cost of a rank-join operator. To our knowledge, this is the first effort in estimating the needed input size for optimal rank aggregation algorithms. Costing ranking plans is key to the full integration of rank-join operators in real-world query processing engines.Since optimal execution strategies picked by static query optimizers lose their optimality due to estimation errors and unexpected changes in the computing environment, we introduce several adaptive execution strategies for top-k queries that respond to these unexpected changes and costing errors. Our reactive reoptimization techniques change the execution plan at runtime to significantly enhance the performance of running queries. Since top-k query plans are usually pipelined and maintain a complex ranking state, altering the execution strategy of a running ranking query is an important and challenging task.We conduct an extensive experimental study to evaluate the performance of the proposed framework. The experimental results are twofold: (1) we show the effectiveness of our cost-based approach of integrating ranking plans in dynamic programming cost-based optimizers; and (2) we show a significant speedup (up to 300%) when using our adaptive execution of ranking plans over the state-of-the-art mid-query reoptimization strategies.

Data management in RFID applications

Nowadays, RFID applications have attracted a great deal of interest due to their increasing adoptions in supply chain management, logistics and security. They have posed many new challenges to existing underlying database technologies, such as the requirements of supporting big volume data, preserving data transition path and handling new types of queries. In this paper, we propose an efficient method to manage RFID data.We explore and take advantage of the containment relationships in the relational tables in order to support special queries in the RFID applications. The experimental evaluation conducted on an existing RDBMS demonstrates the efficiency of our method.

Place: A Distributed Spatio-Temporal Data Stream Management System for Moving Objects

In this paper, we introduce PLACE*, a distributed spatio-temporal data stream management system for moving objects. PLACE* supports continuous spatio-temporal queries that hop among a network of regional servers. To minimize the execution cost, a new Query-Track- Participate (QTP) query processing model is proposed inside PLACE*. In the QTP model, a query is continuously answered by a querying server, a tracking server, and a set of participating servers. In this paper, we focus on query plan generation, execution and update algorithms for continuous range queries in PLACE* using QTP. An extensive experimental study demonstrates the effectiveness of the proposed algorithms in PLACE*.

Spatio-temporal histograms

This paper presents a framework for building and continuously maintaining spatio-temporal histograms (ST-Histograms, for short). ST-Histograms are used for selectivity estimation of continuous pipelined query operators. Unlike traditional histograms that examine and/or sample all incoming data tuples, ST-Histograms are built by monitoring the actual selectivities of the outstanding continuous queries. ST-Histograms have three main features: (1) The ST-Histograms are built with (almost) no overhead to the system. We use only feedback (i.e., the actual selectivity) from the existing continuous queries. (2) Rather than wasting system resources in maintaining accurate histograms for the whole spatial space, we only maintain accurate histograms for that part of the space that is relevant to the current existing queries. The rest of the space has less accurate histograms. (3) The ST-Histograms are equipped with a periodicity detection procedure that predicts the future execution of the continuous queries. Hence, the query processing engine can continuously adapt the continuous query pipeline to reflect this prediction. Experimental results based on a real implementation inside a data stream management system show a superior performance of ST-Histograms in terms of providing accurate operator selectivity estimations with no extra overhead.

Continuous aggregate nearest neighbor queries

This paper addresses the problem of continuous aggregate nearest-neighbor (CANN) queries for moving objects in spatio-temporal data stream management systems. A CANN query specifies a set of landmarks, an integer k, and an aggregate distance function f (e.g., min, max, or sum), where f computes the aggregate distance between a moving object and each of the landmarks. The answer to this continuous query is the set of k moving objects that have the smallest aggregate distance f. A CANN query may also be viewed as a combined set of nearest neighbor queries. We introduce several algorithms to continuously and incrementally answer CANN queries. Extensive experimentation shows that the proposed operators outperform the state-of-the-art algorithms by up to a factor of 3 and incur low memory overhead.

A framework for testing query transformation rules

In order to enable extensibility, modern query optimizers typically leverage a transformation rule based framework. Testing individual rule correctness as well as correctness of rule interactions is crucial in verifying the functionality of a query optimizer. While there has been a lot of work on how to architect optimizers for extensibility using a rule based framework, there has been relatively little work on how to test such optimizers. In this paper we present a framework for testing query transformation rules which enables: (a) efficient generation of queries that exercise a particular transformation rule or a set of rules and (b) efficient execution of corresponding test suites for correctness testing.

TRUPI: Twitter Recommendation Based on Users’ Personal Interests

Twitter has emerged as one of the most powerful micro-blogging services for real-time sharing of information on the web. The large volume of posts in several topics is overwhelming to twitter users who might be interested in only few topics. To this end, we propose TRUPI, a personalized recommendation system for the timelines of twitter users where tweets are ranked by the user’s personal interests. The proposed system combines the user social features and interactions as well as the history of her tweets content to attain her interests. The system captures the users interests dynamically by modeling them as a time variant in different topics to accommodate the change of these interests over time. More specifically, we combine a set of machine learning and natural language processing techniques to analyze the topics of the various tweets posted on the user’s timeline and rank them based on her dynamically detected interests. Our extensive performance evaluation on a publicly available dataset demonstrates the effectiveness of TRUPI and shows that it outperforms the competitive state of the art by 25% on nDCG@25, and 14% on MAP.

Chameleon: Context-Awareness inside DBMSs

Context is any information used to characterize the situation of an entity. Examples of contexts include time, location, identity, and activity of a user. This paper proposes a general context-aware DBMS, named Chameleon, that will eliminate the need for having specialized database engines, e.g., spatial DBMS, temporal DBMS, and Hippocratic DBMS, since space, time, and identity can be treated as contexts in the general context-aware DBMS. In Chameleon, we can combine multiple contexts into more complex ones using the proposed context composition, e.g., a Hippocratic DBMS that also provides spatio-temporal and location contextual services. As a proof of concept, we construct two case studies using the same context-aware DBMS platform within Chameleon. One treats identity as a context to realize a privacy-aware (Hippocratic) database server, while the other treats space as a context to realize a spatial database server using the same proposed constructs and interfaces of Chameleon.

Interactive Fusion and Tracking For Multi-Modal Spatial Data Visualization

Scientific data acquired through sensors which monitor natural phenomena, as well as simulation data that imitate time‐identified events, have fueled the need for interactive techniques to successfully analyze and understand trends and patterns across space and time. We present a novel interactive visualization technique that fuses ground truth measurements with simulation results in real‐time to support the continuous tracking and analysis of spatiotemporal patterns. We start by constructing a reference model which densely represents the expected temporal behavior, and then use GPU parallelism to advect measurements on the model and track their location at any given point in time. Our results show that users can interactively fill the spatio‐temporal gaps in real world observations, and generate animations that accurately describe physical phenomena.