Mohamed E. El-Sharkawi

An extended k-means technique for clustering moving objects

This paper presents a framework aimed at monitoring the behavior of aircraft in a given airspace. Trajectories that constitute typical operations are determined and learned using data-driven methods. Standard procedures are used by air traffic controllers (ATCs) to guide aircraft, ensure the safety of the airspace, and maximize runway occupancy. Even though standard procedures are used by ATCs, control of the aircraft remains with the pilots, leading to large variability in the flight patterns observed. Two methods for identifying typical operations and their variability from recorded radar tracks are presented. This knowledge base is then used to monitor the conformance of current operations against operations previously identified as typical. A tool called AirTrajectoryMiner is presented, aiming at monitoring the instantaneous health of the airspace, in real time. The airspace is “healthy” when all aircraft are flying according to typical operations. A measure of complexity is introduced, measuring the conformance of current flight to typical flight patterns. When an aircraft does not conform, the complexity increases as more attention from ATC is required to ensure safe separation between aircraft.

Vertical Mining of Frequent Patterns from Uncertain Data

Efficient algorithms have been developed for mining frequent patterns in traditional data where the content of each transaction is definitely known. There are many applications that deal with real data sets where the contents of the transactions are uncertain. Limited research work has been dedicated for mining frequent patterns from uncertain data. This is done by extending the state of art horizontal algorithms proposed for mining precise data to be suitable with the uncertainty environment. Vertical mining is a promising approach that is experimentally proved to be more efficient than the horizontal mining. In this paper we extend the state-of-art vertical mining algorithm Eclat for mining frequent patterns from uncertain data producing the proposed UEclat algorithm. In addition, we compared the proposed UEclat algorithm with the UF-growth algorithm. Our experimental results show that the proposed algorithm outperforms the UF-growth algorithm by at least one order of magnitude.

LNV: relational database storage structure for XML documents

Summary form only given. Storing XML documents in a relational database is a promising solution because relational databases are mature and scale very well and they have the additional advantage that in a relational database XML data and traditional (structured) data can coexist making it possible to build applications that involve both kinds of data with little extra effort. In this paper we propose a novel relational database storage structure that can store XML documents in the XML layer of the semantic Web. We present also algorithms that translate XPath queries to SQL queries that run on a relational database management system.

Effects of shaping characteristics on the performance of nested transactions

Abstract The nested transaction model was introduced to satisfy the requirements of advanced database applications. Moreover, it is currently the basic transaction model for new database applications like workflow systems and new database systems like mobile databases and object-relational databases. Though there are several performance evaluation studies of different concurrency control mechanisms in nested transactions, the effects of transaction parameters on the overall system performance have not received any attention. In this paper, we study the effects of transactions characteristics on system performance. We developed a detailed simulation model and conducted several experiments to measure the impact of transactions characteristics on the performance. First, the effect of the number of leaves on the performance of nested transactions is investigated under different shaping parameters. Also, effects of the depth of the transaction tree on the system performance are investigated.

A randomized controlled trial of uterine exteriorization versus in situ repair of the uterine incision during cesarean delivery.

To compare extra‐abdominal repair of the uterine incision at cesarean delivery with in situ repair.

CME: a temporal relational model for efficient coalescing

Coalescing is a data restructuring operation applicable to temporal databases. It merges timestamps of adjacent or overlapping tuples that have identical attribute values. The likelihood that a temporal query employs coalescing is very high. However, coalescing is an expensive and time consuming operation. In this paper, we present a novel temporal relational model through which coalescing becomes quite simple. The basic idea is to augment each time-varying attribute in a temporal relation with two additional attributes that trace changes in values of the corresponding time-varying attribute. One attribute traces changes in values with respect to each individual instance (i.e. tuples having the same key value), while the other attribute traces changes in values globally for all instances (i.e. all tuples in the temporal relation). Using these tracing attributes, coalescing could be easily implemented through a quite simple join-free group-by query. The coalescing query is fully processed and optimized by the underlying database management system.

TMIX: Temporal model for indexing XML documents

Different models have been proposed recently for representing temporal data, tracking historical information and retrieving temporal queries results efficiently. We consider the problem of indexing temporal XML documents. In particular, we propose an indexing scheme that uses a summary structure and a matrix that captures the structural relationships as well as time intervals inside a temporal XML document. We introduce an algorithm to efficiently process all types of temporal queries with any depth using our newly proposed index. We show that our proposed index out-performs the state of the art indices in terms of both query processing time and support for different temporal query types.

Processing universal quantification queries using MapReduce

Universal quantification queries are an interesting type of queries that are used in many applications. Although, universal queries have gained their importance in querying traditional databases that are usually implemented on a single machine; nowadays, the rapid growth in information and the extremely fast increase in the number of Web users have driven the need to migrate to new processing environments that are capable to access, process, store, and maintain huge amounts of valuable data. Thus, the use of cloud emerged as a solution for several big data problems. In this paper, we present a number of computing techniques for processing universal quantification queries on large datasets using the popular MapReduce framework. In addition, we present experimental results that show the speed-up and scale-out properties of our proposed algorithms.

Mining disease integrated ontology

Ontology has become a very vital issue to solve important issues regarding human diseases through data integration of chemical and biological data. Mining such data discovers highly important knowledge about diseases can give an important insight to arrive to new drug targets and assist in personalized medicine. In the current paper, a mining technique for diseases is developed based on integrated ontology and association rule mining algorithm. To perform mining, the semantic web, as a knowledge representation methodology is used to integrate data. In addition, an Ontology Association Rule Mining algorithm (OARM) is developed since existing algorithms cannot be applied because of the ontology nature of data containing several types of relations. To test our performance, prostate cancer data is obtained from NCI, which is related to 279 genes and 89 genes (from prostate cancer pathway).

Dynamic k-means: a clustering technique for moving object trajectories

k-means clustering algorithm is a famous clustering algorithm applied in many applications. However, traditional k-means algorithm assumes that the initial number of centroids is known in advance. This dependence on the number of clusters and the initial choice of the centroids affect both the performance and accuracy of the algorithm. To overcome this problem, in this paper, we propose a heuristic that dynamically calculates k based on the movement patterns in the trajectory dataset and optimally initialises the k centroids. We basically consider distinct similar moving patterns as an initialisation for the number of clusters (k). In addition, we design a scalable tool for mining moving object data through (an architecture composed of) a rich set of cluster refinement modules that operate on top of the moving object database enabling users to analyse trajectory data from different perspectives. We validate our approaches experimentally on both real and synthetic data and test the performance and accuracy of our techniques.