When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

Towards the Detection of Promising Processes by Analysing the Relational Data

Business process discovery provides mechanisms to extract the general process behaviour from event observations. However, not always the logs are available and must be extracted from repositories, such as relational databases. Derived from the references that exist between the relational tables, several are the possible combinations of traces of events that can be extracted from a relational database. Dif ferent traces can be extracted depending on which attribute represents the case−id, what are the attributes that represent the execution of an activity, or how to obtain the timestamp to define the order of the events. This paper proposes a method to analyse a wide range of possible traces that could be extracted from a relational database, based on measuring the level of interest of extracting a trace log, later used for a discov ery process. The analysis is done by means of a set of proposed metrics before the traces are generated and the process is discovered. This anal ysis helps to reduce the computational cost of process discovery. For a possible case−id every possible traces are analysed and measured. To validate our proposal, we have used a real relational database, where the detection of processes (most and least promising) are compared to rely on our proposal.Ministerio de Ciencia y Tecnología RTI2018-094283-B-C3

An introduction to Graph Data Management

A graph database is a database where the data structures for the schema and/or instances are modeled as a (labeled)(directed) graph or generalizations of it, and where querying is expressed by graph-oriented operations and type constructors. In this article we present the basic notions of graph databases, give an historical overview of its main development, and study the main current systems that implement them

Towards interface design for virtual database / Zanariah Idrus...[et al.]

Today, big data has become as one of the important contribution in database management. It led to innovative ways of storing and organizing data which include structured and unstructured data. The unstructured data such as in news, reports, chats and surveys are basically loaded with heavy text data and numerous format. Thus, these data become challenging to be used for diverse purpose and are not appropriate to be stored in database. However, virtual database method has the capability to organize the unstructured data, and reconstruct into firm and concrete data. This approach carry out two major processes in databases which are mining and managing the data. However, the main problem is the insufficient support between people using databases and the heap of data collection. This is due to unawareness of clustered data organization as information is stored implicitly. Thus, this paper presents the conception of clustered data using the interface design model. Alignment of features and connections between the interface and knowledge composition allow users to access knowledge proficiently

Hybrid Information Retrieval Model For Web Images

The Bing Bang of the Internet in the early 90's increased dramatically the number of images being distributed and shared over the web. As a result, image information retrieval systems were developed to index and retrieve image files spread over the Internet. Most of these systems are keyword-based which search for images based on their textual metadata; and thus, they are imprecise as it is vague to describe an image with a human language. Besides, there exist the content-based image retrieval systems which search for images based on their visual information. However, content-based type systems are still immature and not that effective as they suffer from low retrieval recall/precision rate. This paper proposes a new hybrid image information retrieval model for indexing and retrieving web images published in HTML documents. The distinguishing mark of the proposed model is that it is based on both graphical content and textual metadata. The graphical content is denoted by color features and color histogram of the image; while textual metadata are denoted by the terms that surround the image in the HTML document, more particularly, the terms that appear in the tags p, h1, and h2, in addition to the terms that appear in the image's alt attribute, filename, and class-label. Moreover, this paper presents a new term weighting scheme called VTF-IDF short for Variable Term Frequency-Inverse Document Frequency which unlike traditional schemes, it exploits the HTML tag structure and assigns an extra bonus weight for terms that appear within certain particular HTML tags that are correlated to the semantics of the image. Experiments conducted to evaluate the proposed IR model showed a high retrieval precision rate that outpaced other current models.Comment: LACSC - Lebanese Association for Computational Sciences, http://www.lacsc.org/; International Journal of Computer Science & Emerging Technologies (IJCSET), Vol. 3, No. 1, February 201

The IGN-E case: Integrating through a hidden ontology

National Geographic Institute of Spain (IGN-E) wanted to integrate its main information sources for building a common vocabulary reference and thus to manage the huge amount of information it held. The main problem of this integration is the great heterogeneity of data sources. The Ontology Engineering Group (OEG) is working with IGN-E to attain this objective in two phases: first, by creating automatically an ontology using the semantics of catalogues sections, and second, by discovering mappings automatically that can relate ontology concepts to database instances. So, these mappings are the instruments to break the syntactic, semantic and granularity heterogeneity gap. We have developed software for building a first ontology version and for discovering automatically mappings using techniques that take into account all types of heterogeneity. The ontology contains a set of extra-attributes which are identified in the building process. The ontology, called PhenomenOntology, will be reviewed by domain experts of IGN-E. The automatic mapping discovery will be also used for discovering new knowledge that will be added to the ontology. For increasing the usability and giving independence to different parts, the processes of each phase will be designed automatically and as upgradeable as possible