Alaaeldin M. Hafez

Itemset trees for targeted association querying

Association mining techniques search for groups of frequently co-occurring items in a market-basket type of data and turn these groups into business-oriented rules. Previous research has focused predominantly on how to obtain exhaustive lists of such associations. However, users often prefer a quick response to targeted queries. For instance, they may want to learn about the buying habits of customers that frequently purchase cereals and fruits. To expedite the processing of such queries, we propose an approach that converts the market-basket database into an itemset tree. Experiments indicate that the targeted queries are answered in a time that is roughly linear in the number of market baskets, N. Also, the construction of the itemset tree has O(N) space and time requirements. Some useful theoretical properties are proven.

Dynamic data mining

Business information received from advanced data analysis and data mining is a critical success factor for companies wishing to maximize competitive advantage. The use of traditional tools and techniques to discover knowledge is ruthless and does not give the right information at the right time. Data mining should provide tactical insights to support the strategic directions. In this paper, we introduce a dynamic approach that uses knowledge discovered in previous episodes. The proposed approach is shown to be effective for solving problems related to the efficiency of handling database updates, accuracy of data mining results, gaining more knowledge and interpretation of the results, and performance. Our results do not depend on the approach used to generate itemsets. In our analysis, we have used an Apriori-like approach as a local procedure to generate large itemsets. We prove that the Dynamic Data Mining algorithm is correct and complete.

The Item-Set Tree: A Data Structure for Data Mining

Enhancements in data capturing technology have lead to exponential growth in amounts of data being stored in information systems. This growth in turn has motivated researchers to seek new techniques for extraction of knowledge implicit or hidden in the data. In this paper, we motivate the need for an incremental data mining approach based on data structure called the item-set tree. The motivated approach is shown to be effective for solving problems related to efficiency of handling data updates, accuracy of data mining results, processing input transactions, and answering user queries. We present efficient algorithms to insert transactions into the item-set tree and to count frequencies of itemsets for queries about strength of association among items. We prove that the expected complexity of inserting a transaction is ≅ O(1), and that of frequency counting is O(n), where n is the cardinality of the domain of items.

Selection criteria for text mining approaches

Text mining include several techniques like categorization of text, clustering, etc.Text mining techniques can be used to finding useful information from documents.We propose some criteria to evaluate the effectiveness of text mining techniques.These proposed criteria can facilitate the selection of appropriate technique. Text mining techniques include categorization of text, summarization, topic detection, concept extraction, search and retrieval, document clustering, etc. Each of these techniques can be used in finding some non-trivial information from a collection of documents. Text mining can also be employed to detect a documents main topic/theme which is useful in creating taxonomy from the document collection. Areas of applications for text mining include publishing, media, telecommunications, marketing, research, healthcare, medicine, etc. Text mining has also been applied on many applications on the World Wide Web for developing recommendation systems. We propose here a set of criteria to evaluate the effectiveness of text mining techniques in an attempt to facilitate the selection of appropriate technique.

A Theoretical Framework for Association Mining Based on the Boolean Retrieval Model

Data mining has been defined as the non-trivial extraction of implicit, previously unknown and potentially useful information from data. Association mining is one of the important sub-fields in data mining, where rules that imply certain association relationships among a set of items in a transaction database are discovered. The efforts of most researchers focus on discovering rules in the form of implications between itemsets, which are subsets of items that have adequate supports. Having itemsets as both antecedent and precedent parts was motivated by the original application pertaining to market baskets and they represent only the simplest form of predicates. This simplicity is also due in part to the lack of a theoretical framework that includes more expressive predicates. The framework we develop derives from the observation that information retrieval and association mining are two complementary processes on the same data records or transactions. In information retrieval, given a query, we need to find the subset of records that matches the query. In contrast, in data mining, we need to find the queries (rules) having adequate number of records that support them. In this paper we introduce the theory of association mining that is based on a model of retrieval known as the Boolean Retrieval Model. The potential implications of the proposed theory are presented.

Lip Reading Based Surveillance System

In this paper we propose a lip reading recognition technique designed to be a part of a surveillance system and will be used for physical security. The proposed technique would be used for security issues using motion estimation analysis, applying a new five step search block matching algorithm.. The proposed technique is characterized by high speed performance suitable for real time applications. The three-step search (TSS) algorithm has been widely used as the motion estimation technique in some low bit-rate video compression applications, owing to its simplicity and effectiveness. However, TSS uses a uniformly allocated checking point pattern in its first step, which becomes inefficient for the estimation of small motions. The lip recognition technique deemed valuable in fast recognition of lip reading that can be used in security paradigm for real time applications like password entries that are shoulder surfing resilient.

Task Scheduling in Cloud Computing using Lion Optimization Algorithm

Cloud computing has spread fast because of its high performance distributed computing. It offers services and access to shared resources to internet users through service providers. Efficient performance of task scheduling in clouds is one of the most important research issues which needs to be focused on. Various task scheduling algorithms for cloud based on metaheuristic techniques have been examined and showed high performance in reasonable time such as scheduling algorithms based on Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). In this paper, we propose a new task-scheduling algorithm based on Lion Optimization Algorithm (LOA), for cloud computing. LOA is a nature-inspired population-based algorithm for obtaining global optimization over a search space. It was proposed by Maziar Yazdani and Fariborz Jolai in 2015. It is a metaheuristic algorithm inspired by the special lifestyle of lions and their cooperative characteristics. The proposed task scheduling algorithm is compared with scheduling algorithms based on Genetic Algorithm and Particle Swarm Optimization. The results demonstrate the high performance of the proposed algorithm, when compared with the other algorithms.

An Enhanced Workflow Scheduling Algorithm in Cloud Computing

Cloud Computing has gained high attention by provisioning resources and software as a service. Throughout the years, the number of users of clouds is increasing and thus will increase the number of tasks and load in the cloud. Therefore, scheduling tasks efficiently and dynamically is a critical problem to be solved. There are many scheduling algorithms that are used in cloud computing but most of them are concentrating on minimizing time and cost and some of them concentrate on increasing fault tolerance. However, very few scheduling algorithms that considers time, cost, and fault tolerance at the same time. Moreover, Considering pricing models in developing scheduling algorithms to provide cost-effective fault tolerant techniques is still in its infancy. Therefore, analysing the impact of the different pricing models on scheduling algorithm will lead to choosing the right pricing model that will not affect the cost. This paper proposes developing a scheduling algorithm that combines these features to provide an efficient mapping of tasks and improve Quality of Service (QoS).

Authorization as a Service in Cloud Environments

As more data are stored in clouds, the demand to protect sensitive data against unauthorized access is increased. However, a failure to do so will limit the evolution of cloud computing. In this work, we address this issue by introducing a privacy aware model for authorization as a service to protect the confidentiality of data, along with an access control policy. We solve the authorization conflicts and inconsistencies and support more complex policies, which are usually required in real applications.

DynTARM: An In-Memory Data Structure for Targeted Strong and Rare Association Rule Mining over Time-Varying Domains

Recently, with companies and government agencies saving large repositories of time stream/temporal data, there is a large push for adapting association rule mining algorithms for dynamic, targeted querying. In addition, issues with data processing latency and results depreciating in value with the passage of time, create a need for swifter and more efficient processing. The aim of targeted association mining is to find potentially interesting implications in large repositories of data. Using targeted association mining techniques, specific implications that contain items of user interest can be found faster and before the implications have depreciated in value beyond usefulness. In this paper, the DynTARM algorithm is proposed for the discovery of targeted and rare association rules. DynTARM has the flexibility to discover strong and rare association rules from data streams within the users sphere of interest. By introducing a measure, called the Volatility Index, to assess the fluctuation in the confidence of rules, rules conforming to different temporal patterns are discovered.