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

Characterizing covers of functional dependencies using FCA

Functional dependencies (FDs) can be used for various important operations on data, for instance, checking the consistency and the quality of a database (including databases that contain complex data). Consequently, a generic framework that allows mining a sound, complete, non-redundant and yet compact set of FDs is an important tool for many different applications. There are different definitions of such sets of FDs (usually called cover). In this paper, we present the characterization of two different kinds of covers for FDs in terms of pattern structures. The convenience of such a characterization is that it allows an easy implementation of efficient mining algorithms which can later be easily adapted to other kinds of similar dependencies. Finally, we present empirical evidence that the proposed approach can perform better than state-ofthe-art FD miner algorithms in large databases.Peer ReviewedPostprint (published version

Characterizing approximate-matching dependencies in formal concept analysis with pattern structures

Functional dependencies (FDs) provide valuable knowledge on the relations between attributes of a data table. A functional dependency holds when the values of an attribute can be determined by another. It has been shown that FDs can be expressed in terms of partitions of tuples that are in agreement w.r.t. the values taken by some subsets of attributes. To extend the use of FDs, several generalizations have been proposed. In this work, we study approximatematching dependencies that generalize FDs by relaxing the constraints on the attributes, i.e. agreement is based on a similarity relation rather than on equality. Such dependencies are attracting attention in the database field since they allow uncrisping the basic notion of FDs extending its application to many different fields, such as data quality, data mining, behavior analysis, data cleaning or data partition, among others. We show that these dependencies can be formalized in the framework of Formal Concept Analysis (FCA) using a previous formalization introduced for standard FDs. Our new results state that, starting from the conceptual structure of a pattern structure, and generalizing the notion of relation between tuples, approximate-matching dependencies can be characterized as implications in a pattern concept lattice. We finally show how to use basic FCA algorithms to construct a pattern concept lattice that entails these dependencies after a slight and tractable binarization of the original data.Postprint (author's final draft

OntoDSumm : Ontology based Tweet Summarization for Disaster Events

The huge popularity of social media platforms like Twitter attracts a large fraction of users to share real-time information and short situational messages during disasters. A summary of these tweets is required by the government organizations, agencies, and volunteers for efficient and quick disaster response. However, the huge influx of tweets makes it difficult to manually get a precise overview of ongoing events. To handle this challenge, several tweet summarization approaches have been proposed. In most of the existing literature, tweet summarization is broken into a two-step process where in the first step, it categorizes tweets, and in the second step, it chooses representative tweets from each category. There are both supervised as well as unsupervised approaches found in literature to solve the problem of first step. Supervised approaches requires huge amount of labelled data which incurs cost as well as time. On the other hand, unsupervised approaches could not clusters tweet properly due to the overlapping keywords, vocabulary size, lack of understanding of semantic meaning etc. While, for the second step of summarization, existing approaches applied different ranking methods where those ranking methods are very generic which fail to compute proper importance of a tweet respect to a disaster. Both the problems can be handled far better with proper domain knowledge. In this paper, we exploited already existing domain knowledge by the means of ontology in both the steps and proposed a novel disaster summarization method OntoDSumm. We evaluate this proposed method with 4 state-of-the-art methods using 10 disaster datasets. Evaluation results reveal that OntoDSumm outperforms existing methods by approximately 2-66% in terms of ROUGE-1 F1 score

Survey on Challenges of Question Answering in the Semantic Web

Höffner K, Walter S, Marx E, Usbeck R, Lehmann J, Ngomo A-CN. Survey on Challenges of Question Answering in the Semantic Web. Semantic Web Journal. 2017;8(6):895-920

Seamless Integration of RESTful Services into the Web of Data

We live in an era of ever-increasing abundance of data. To cope with the information overload we suffer from every single day, more sophisticated methods are required to access, manipulate, and analyze these humongous amounts of data. By embracing the heterogeneity, which is unavoidable at such a scale, and accepting the fact that the data quality and meaning are fuzzy, more adaptable, flexible, and extensible systems can be built. RESTful services combined with Semantic Web technologies could prove to be a viable path to achieve that. Their combination a1lows data integration on an unprecedented sca1e and solves some of the problems Web developers are continuously struggling with. This paper introduces a novel approach to create machine-readable descriptions for RESTful services as a first step towards this ambitious goal. It also shows how these descriptions along with analgorithm to translate SPARQL queries to HTTP requests can be used to integrate RESTful services into a global read-write Web of Data

Keys and Pseudo-keys Detection for Web Datasets Cleansing and Interlinking

scharffe2012bThis report introduces a novel method for analysing web datasets based on key dependencies. This particular kind of functional dependencies, widely studied in the field of database theory, allows to evaluate if a set of properties constitutes a key for the set of data considered. When this is the case, there won't be any two instances having identical values for these properties. After giving necessary definitions, we propose an algorithm for detecting minimal keys and pseudo-keys in a RDF dataset. We then use this algorithm to detect keys in datasets published as web data and we apply this approach in two applications: (i) reducing the number of properties to compare in order to discover equivalent instances between two datasets, (ii) detecting errors inside a dataset

Using an ontology to improve the web search experience

The search terms that a user passes to a search engine are often ambiguous, referring to homonyms. The results in these cases are a mixture of links to documents that contain different meanings of the search terms. Current search engines provide suggested query completions in a dropdown list. However, such lists are not well organized, mixing completions for different meanings. In addition, the suggested search phrases are not discriminating enough. Moreover, current search engines often return an unexpected number of results. Zero hits are naturally undesirable, while too many hits are likely to be overwhelming and of low precision. This dissertation work aims at providing a better Web search experience for the users by addressing the above described problems.To improve the search for homonyms, suggested completions are well organized and visually separated. In addition, this approach supports the use of negative terms to disambiguate the suggested completions in the list. The dissertation presents an algorithm to generate the suggested search completion terms using an ontology and new ways of displaying homonymous search results. These algorithms have been implemented in the Ontology-Supported Web Search (OSWS) System for famous people. This dissertation presents a method for dynamically building the necessary ontology of famous people based on mining the suggested completions of a search engine. This is combined with data from DBpedia. To enhance the OSWS ontology, Facebook is used as a secondary data source. Information from people public pages is mined and Facebook attributes are cleaned up and mapped to the OSWS ontology. To control the size of the result sets returned by the search engines, this dissertation demonstrates a query rewriting method for generating alternative query strings and implements a model for predicting the number of search engine hits for each alternative query string, based on the English language frequencies of the words in the search terms. Evaluation experiments of the hit count prediction model are presented for three major search engines. The dissertation also discusses and quantifies how far the Google, Yahoo! and Bing search engines diverge from monotonic behavior, considering negative and positive search terms separately

A semantic sensor web for environmental decision support applications

Sensing devices are increasingly being deployed to monitor the physical world around us. One class of application for which sensor data is pertinent is environmental decision support systems, e.g., flood emergency response. For these applications, the sensor readings need to be put in context by integrating them with other sources of data about the surrounding environment. Traditional systems for predicting and detecting floods rely on methods that need significant human resources. In this paper we describe a semantic sensor web architecture for integrating multiple heterogeneous datasets, including live and historic sensor data, databases, and map layers. The architecture provides mechanisms for discovering datasets, defining integrated views over them, continuously receiving data in real-time, and visualising on screen and interacting with the data. Our approach makes extensive use of web service standards for querying and accessing data, and semantic technologies to discover and integrate datasets. We demonstrate the use of our semantic sensor web architecture in the context of a flood response planning web application that uses data from sensor networks monitoring the sea-state around the coast of England