María Bermúdez-Edo

A Knowledge-Based Approach for Real-Time IoT Data Stream Annotation and Processing

Internet of Things is a generic term that refers to interconnection of real-world services which are provided by smart objects and sensors that enable interaction with the physical world. Cities are also evolving into large interconnected ecosystems in an effort to improve sustainability and operational efficiency of the city services and infrastructure. However, it is often difficult to perform real-time analysis of large amount of heterogeneous data and sensory information that are provided by various sources. This paper describes a framework for real-time semantic annotation of streaming IoT data to support dynamic integration into the Web using the Advanced Message Queuing Protocol (AMPQ). This will enable delivery of large volume of data that can influence the performance of the smart city systems that use IoT data. We present an information model to represent summarisation and reliability of stream data. The framework is evaluated with the data size and average exchanged message time using summarised and raw sensor data. Based on a statistical analysis, a detailed comparison between various sensor points is made to investigate the memory and computational cost for the stream annotation framework.

IoT-Lite: A Lightweight Semantic Model for the Internet of Things

Over the past few years the semantics community has developed ontologies to describe concepts, relationships between different entities in various application domains, including Internet of Things (IoT) applications. A key problem is that most of the IoT related semantic descriptions are not as widely adopted as expected. One of the main concerns of users, developers is that semantic techniques increase the complexity, processing time, therefore they are unsuitable for dynamic, responsive environments such as the IoT. To address this concern, we propose IoT-Lite, an instantiation of the semantic sensor network (SSN) ontology to describe key IoT concepts allowing interoperability, discovery of sensory data in heterogeneous IoT platforms by a lightweight semantics. We propose 10 rules for good, scalable semantic model design, follow them to create IoT-Lite. We also demonstrate the scalability of IoT-Lite by providing some experimental analysis,, assess IoT-Lite against another solution in terms of round time trip (RTT) performance for query-response times.

Challenges for Quality of Data in Smart Cities

Smart cities use multimodal information coming from heterogeneous sources, including various types of the Internet of Things (IoT) data such as traffic, weather, pollution, and noise data. The smart city data usually have different quality of information (QoI). QoI of each data source mainly depends on three factors: (1) errors in measurements or precision of the data collection devices, (2) noise in the environment and quality of data communication and processing (including network-dependent quality of service parameters), and (3) granularity of the observations and measurements in both spatial and temporal dimensions. Furthermore, various environments have different requirements that will determine the efficacy of using the data in the smart city applications; some systems have energy restrictions; and some wireless networks could rely on low bandwidth or intermittent connectivity. Most smart city applications also have to deal with huge volumes of data, with high velocity, dynamicity, and a variety of types of data. The QoI issues become more challenging when various data with different QoI are going to be integrated into an application to extract higher-level information and/or to provide actionable information to other services and applications. In some of the current smart city frameworks, the underlying information model is based on semantic

Analyzing a firm's international portfolio of technological knowledge: A declarative ontology-based OWL approach for patent documents

Patent databases contain large amounts of information about the inventions and metadata of corporate patents (such as the technological domain they belong to, their applicants, and inventors). These databases are available online but since they do not provide explicit information about the relationships between different patent metadata, it is not possible for computers to automatically process such relationships. Several patent ontologies have been proposed so far in order to provide patent knowledge bases with semantics by merging information from different databases and establishing a common vocabulary. However, previous ontology literature has paid limited attention to the representation of specific relationships among metadata and the design of reasoning procedures that would allow some information not explicitly specified in the databases or ontologies to be inferred. This article proposes a methodological approach for the definition of relationships and reasoning tasks for patent analysis by using patent ontologies, and provides a real illustration of its potential in the context of international flows of research knowledge. This declarative method is based on the formal definition of key patent analysis indicators (KPAIs). The case study analysis is relevant because global competition and the importance of multinational firms in the patent process have resulted in firms not only patenting on their domestic markets but also transferring their patents to other markets and developing patents in different countries. In this context, it is important to analyze the connections between the patenting processes and the international knowledge flows of research and development. More specifically, the paper illustrates the applicability of the proposed methodology by classifying patents into the five patterns of internationalization identified by the Organization for Economic Co-operation and Development (OECD).

On the Effect of Adaptive and Nonadaptive Analysis of Time-Series Sensory Data

With the growing popularity of information and communications technologies and information sharing and integration, cities are evolving into large interconnected ecosystems by using smart objects and sensors that enable interaction with the physical world. However, it is often difficult to perform real-time analysis of large amount on heterogeneous data and sensory information that are provided by various resources. This paper describes a framework for real-time semantic annotation and aggregation of data streams to support dynamic integration into the Web using the advanced message queuing protocol. We provide a comprehensive analysis on the effect of adaptive and nonadaptive window size in segmentation of time series using SensorSAX and symbolic aggregate approximation (SAX) approaches for data streams with different variation and sampling rate in real-time processing. The framework is evaluated with three parameters, namely window size parameter of the SAX algorithm, sensitivity level, and minimum window size parameters of the SensorSAX algorithm based on the average data aggregation and annotation time, CPU consumption, data size, and data reconstruction rate. Based on a statistical analysis, a detailed comparison between various sensor points is made to investigate the memory and computational cost of the stream-processing framework. Our results suggests that regardless of utilized segmentation approach, due to the fact that each geographically different sensory environment has got a different dynamicity level, it is desirable to find the optimal data aggregation parameters in order to reduce the energy consumption and improve the data aggregation quality.

Proposals on assessment environments for anomaly-based network intrusion detection systems

One of the key challenges that researchers should face when proposing a new intrusion detection approach (IDS) is that of demonstrating its general validity. This fact goes necessarily through the disposal of a real set of intrusion (as well as non-intrusion) related events, from which to compare and thus validate the performance of the novel proposed techniques. However, this a priori simple issue is far to be obvious because of the lack of a commonly accepted assessment methodology. In this line, the authors discuss a set of basic requirements that an intrusion-oriented framework should fulfill in order to deal with the normalization of the evaluation process in IDS environments. In its current preliminary state, the work is mainly focused to analyze, specify and manage traffic databases for developing and validating NIDS.

IoT-Lite: a lightweight semantic model for the internet of things and its use with dynamic semantics

Over the past few years, the semantics community has developed several ontologies to describe concepts and relationships for internet of things (IoT) applications. A key problem is that most of the IoT-related semantic descriptions are not as widely adopted as expected. One of the main concerns of users and developers is that semantic techniques increase the complexity and processing time, and therefore, they are unsuitable for dynamic and responsive environments such as the IoT. To address this concern, we propose IoT-Lite, an instantiation of the semantic sensor network ontology to describe key IoT concepts allowing interoperability and discovery of sensory data in heterogeneous IoT platforms by a lightweight semantics. We propose 10 rules for good and scalable semantic model design and follow them to create IoT-Lite. We also demonstrate the scalability of IoT-Lite by providing some experimental analysis and assess IoT-Lite against another solution in terms of round trip time performance for query-response times. We have linked IoT-Lite with stream annotation ontology, to allow queries over stream data annotations, and we have also added dynamic semantics in the form of MathML annotations to IoT-Lite. Dynamic semantics allows the annotation of spatio-temporal values, reducing storage requirements and therefore the response time for queries. Dynamic semantics stores mathematical formulas to recover estimated values when actual values are missing.

The Influence of International Scope on the Relationship Between Patented Environmental Innovations and Firm Performance

The literature on the natural-resource-based view of firms has mostly focused on the positive relationship between financial performance and environmental innovation. The present study extends this research by addressing recent calls to identify the specific managerial approaches that affect a firm’s ability to financially benefit from an innovative environmental strategy. In particular, the focus is on how the selected international scope of patented environmental innovations affects a firms’ financial performance. The sample used included a 5-year data panel of 3,087 environmental patent applications by the 79 Information and Communication Technology firms in the Financial Times Global 500 firms list. The findings indicate that the geographical scope of the exploitation of environmental patents increases the positive relationship between patented environmental innovation and financial performance whereas the geographical scope of knowledge sourcing of environmental patents does reduce this performance.

Managing technological knowledge of patents: HCOntology, a semantic approach

Abstract Patent data provide technological information essential to define strategies and decisions in the context of firm innovative processes. At present, information regarding patents is usually represented and stored in large databases. Information from these databases is commonly retrieved in the form of files with a CSV- or XML-based codification but with little semantics that enable the inference of further relationships among patents. In these databases, each patent is associated with a technological field by a code. Although the codes assume a hierarchical classification approach, inclusion/subsumption relationships are not explicitly specified such that computers can process them automatically. In recent years, ontologies have been proven to facilitate the exchange of information between people and systems. In this context, the Web Ontology Language (OWL), whose formal semantics are based on description logics, has become the most widely used language for the representation of ontologies. Certain patent ontologies have already been developed in OWL to benefit from the semantics of patent information. However, none have fully exploited the information that can be derived from the formal representation of patent code classification hierarchies through description-logics-based reasoning. This paper presents an approach to automatically translate the hierarchies found in the patent classification codes into concept hierarchies. This proposal also enables the automatic inference of implicit knowledge based on reclassification techniques and relationships between different application domains without changing the applications that make use of patent information. Several examples are presented to illustrate the applicability of the proposal and how it can assist firms in patent information management.

Semantic Patent Information Retrieval and Management with OWL

Patent information is mainly represented and stored in databases containing large amounts of information about the inventions and metadata of patents such as the technological field to which they belong, which can be retrieved in standard formats such as CSV or XML. These, however, provide few semantics to enable further relationships among patents to be inferred for analysis purposes. Ontologies, mostly represented in the Web Ontology Language (OWL), are increasingly being developed to represent, manage and reason about data in information systems. Unfortunately, the current patent ontologies do not seem to fully capture the implicit hierarchies present in patent technology codes to exploit the information that can be derived from the formal representation of patent code classification hierarchies through logic reasoning. This paper presents an approach to automatically index hierarchical codes with ontological categories and enrich the information retrieved and knowledge management from different patent repositories with new relationships, properties and inferred information.