A Dual-Mode Intelligent Shopping Assistant

Radio Frequency Identification” (RFID) promises to enable an automatic collection of shopping data for business intelligent purposes. Three important components of intelligence have been identified for systems; ability to predict, ability to adapt and ability to take appropriate action. While extensive research has been done on shopping assistants and the use of RFID in the grocery industry, there is still a lack of insight into the benefits of processing shopping data within a Business Intelligence infrastructure and the components of intelligence. This paper presents iShopper, an intelligent dual-mode shopping assistant. The main objective of iShopper is to create new shopping incentives through exploiting the high penetration of mobile communications, as well as the opportunities that emerging automatic product identification technologies and business intelligence offer to the grocery industry. The results indicate that the role of Business Intelligence components and RFID technology in shopping should not be underestimate

Semantic content-based video retrieval

The development in multimedia technology has brought the use of video documents to personal computers. The increased volume of multimedia data available in everyday lives has dramatically adopted these technologies for storing that multimedia data. Now these everyday live environments demand sophisticated systems for management and effective systems for the search and retrieval of multimedia data. This thesis presents a semantic content-based video retrieval system. This work focuses on the semantic content of video documents and describes the implementation of a semantic-based video indexing and retrieval system suitable for the video-on-demand style applications. This thesis addresses issues related to developing a model for describing the semantic content of a video document and representing information about this content. It develops a sophisticated semantic video model that expresses the underlying semantic structure of a video document and retrieves video clips among different levels of details. The proposed semantic model is an extension of the traditional conceptual model which will be applied to the video domain. The semantic video model describes how the metadata can be represented. The metadata contain information on the semantic video structure, the high-level semantics composition of elementary semantic units, and the video content indexing and storage. The proposed model divides a video document based on its semantic content into a structure of story, events, activities and objects with interrelationships in the various spaces in the video (time, space, context and structure). Semantic content-based video retrieval demands human and machine understanding of video content. This thesis investigates and suggests a methodology suitable for integrating manual human understanding and automatic machine understanding technologies of video documents. A computer-aided semantic video analyzer, which utilizes the processing techniques for semantic video acquisition, is simulated. This thesis proposes a video query language based on the first order logic for querying video information, and a design and an implementation for video retrieval. This language will provide operations for utilizing compositional data, description, and contextual, spatial and temporal relationships in the user\u27s queries. This thesis also introduces a graphical conceptual model to describe the relations among semantic units constituting a composite unit which is a step toward an easy-to-grasp graphical user interface. The results of this thesis lead to the conclusion that: • A video document has a rich internal semantic structure that can be formally expressed and used for semantic content-based video retrieval. • It is possible to construct a semantic based video indexing system and a computer-aided analyzer to assist in semantic video analysis and acquisition. • It is possible to retrieve video documents based on their semantic content. The author considers this work a step toward making video documents searchable as text

Application of semistructured data model to the implementation of semantic content-based video retrieval system

Semantic indexing of a video document is a process that performs the identification of elementary and complex semantic units in the indexed document in order to create a semantic index defined as a mapping of semantic units into the sequences of video frames. Semantic content-based video retrieval system is a software system that uses a semantic index built over a collection of video documents to retrieve the sequences of video frames that satisfy the given conditions. This work introduces a new multilevel view of data for the semantic content-based video retrieval systems. At the topmost level, we define an abstract view of data and we express it in a notation of enhanced conceptual modeling suitable for the formal representation of the semantic contents of video documents. A semistructured data model is proposed for the middle level representation of data. At the bottom level we implement a semistructured data model as an object-relational database. The completeness of the proposed approach is demonstrated through the mappings of a conceptual level into a semistructured level and into an object-relational organization of data. The paper describes a system of operations on semistructured data and shows how a sample query can be represented as an expression built from the operations

A Multi Join Algorithm Utilizing Double Indices

Join has always been one of the most expensive queries to carry out in terms of the amount of time to process. This paper introduces a novel multi join algorithm to join multiple relations. The novel algorithm is based on a hashed-based join of two relations to produce a double index. This is done by scanning the two relations once. Instead of moving the records into buckets, a double index is built. This will eliminate collision as a result of a complete hash algorithm. The double index will be divided into join buckets of similar categories from the two relations. Buckets with similar keys are joined to produce joined buckets. This will lead at the end to a complete join index of the two relations without actually joining the actual relations. The time complexity required to build the join index of two categories is O(m log m) where m is the size of each category. The proposed algorithm has a time complexity of O (n log m) for all buckets where n is the number of buckets. The join index will be used to materialize the joined relation if required. Otherwise, along with other join indices of other relations, the join index builds a lattice to be used in multi-join operations with minimal I/O requirements. The lattice of the join indices can be fitted into the main memory to reduce time complexity of the multi join algorithm

Evaluation of Metamap Performance in Radiographic Images Retrieval

El presente artículo muestra una metodología que busca disminuir las pérdidas de tipo técnico en los ramales secundarios de los sistemas de distribución en sistemas trifásicos equilibrados. Su aplicación en sistemas desbalanceados conserva la misma lógica. Se considera el transformador con cambiador de derivaciones y distintos tipos de carga. Para la asignación del Tap óptimo se usan reglas difusas que consiguen minimizar las perdidas sin violar los límites de tensión. Se presentan resultados sobre un sistema de prueba donde se mejoran los valores de pérdidas cuyo costo se reduce al pago de personal que efectúe las modificaciones del Tap

Quantum behaved particle swarm optimization for data clustering with multiple objectives

Abstract—Clustering is an important tool in many fields such as exploratory data mining and pattern recognition. It consists in organizing a large data set into groups of objects that are more similar to each other than to those in other groups. Despite its use for over three decades, it is still subject to a lot of controversy. In this paper, we cast clustering as a Pareto based multi-objective optimization problem which is handled using a quantum behaved particle swarm optimization algorithm. The search process is carried out over the space of cluster centroids with the aim to find partitions that optimize two objectives simultaneously, namely compactness and connectivity. Global best leader selection is performed using a hybrid method based on sigma values and crowding distance. The proposed algorithm has been tested using synthetic and real data sets and compared to the state of the art methods. The results obtained are very competitive and display good performance both in terms of the cluster validity measure and in terms of the ability to find trade-off partitions especially in the case of close clusters. Keywords- multi objective optimization, quantum behaved particle swarm optimization, clustering, F-measure. I