Manfred Hauswirth

The Graph of Things

The Internet of Things (IoT) with billions of connected devices has been generating an enormous amount of data every hour. Connecting every data item generated by IoT to the rest of the digital world to turn this data into meaningful actions will create new capabilities, richer experiences, and unprecedented economic opportunities for businesses, individuals, and countries. However, providing an integrated view for exploring and querying such data at real-time is extremely challenging due to its Big Data natures: big volume, fast real-time update and messy data sources. To address this challenge, we provide a unified integrated and live view for heterogeneous IoT data sources using Linked Data, called the Graph of Things (GoT). GoT is backed by a scalable and elastic software stack to deal with billions of records of historical and static datasets in conjunction with millions of triples being fetched and enriched to connect to GoT per hour in real time. GoT makes approximately a half of million stream data sources queryable via a SPARQL endpoint and a continuous query channel that enable us to create a live explorer of GoT (http://graphofthings.org/) with just HTML and Javascript.

Physical-Cyber-Social Computing: Looking Back, Looking Forward

Physical-cyber-social (PCS) computing involves a holistic treatment of data, information, and knowledge from the physical, cyber, and social worlds to integrate, understand, correlate, and provide contextually relevant abstractions to humans and the applications that serve them. PCS computing extends current progress in cyber-physical, socio-technical, and cyber-social systems. Here, the guest editors consider powerful ways to exploit data available through various Internet of Things (IoT), citizen and social sensing, Web, and open data sources that are seeing explosive growth. This special issue highlights a variety of PCS applications, such as smart firefighting, intelligent infrastructure, and user guidance in an airport.

The Ubiquitous Semantic Web: Promises, Progress and Challenges

The Semantic Web represents an evolution of the World Wide Web towards one of entities and their relationships, rather than pages and links. Such a progression makes it possible to represent, integrate, query and reason about structured online data. Recent years have witnessed tremendous growth of mobile computing, represented by the widespread adoption of smart phones and tablets. The versatility of such smart devices and the capabilities of semantic technologies form a great foundation for a ubiquitous Semantic Web that will contribute to further realising the true potential of both disciplines. In this paper, the authors argue for values provided by the ubiquitous Semantic Web using a mobile service discovery scenario. They also provide a brief overview of state-of-the-art research in this emerging area. Finally, the authors conclude with a summary of challenges and important research problems.

RDF stream processing with CQELS framework for real-time analysis

This paper presents a solution to the Grand Challenge using CQELS (Continuous Query Evaluation over Linked Stream), a general execution framework to build RDF Stream Processing engines to answer continuous analytical queries. It provides an efficient execution architecture whereby incremental computing algorithms can be implemented to boost the performance. Our experimental results show strong effects of the implemented approach as CQELS outperforms a base-line implementation which recomputes on every incoming input.

Optimizing SPARQL Query Processing on Dynamic and Static Data Based on Query Time/Freshness Requirements Using Materialization

To integrate various Linked Datasets, data warehousing and live query processing provide two extremes for optimized response time and quality respectively. The first approach provides very fast responses but with low-quality because changes of original data are not immediately reflected on materialized data. The second approach provides accurate responses but it is notorious for long response times. A hybrid SPARQL query processor provides a middle ground between two specified extremes by splitting the triple patterns of SPARQL query between live and local processors based on a predetermined coherence threshold specified by the administrator. Considering quality requirements while splitting the SPARQL query, enables the processor to eliminate the unnecessary live execution and releases resources for other queries. This requires estimating the quality of response provided with current materialized data, compare it with user requirements and determine the most selective sub-queries which can boost the response quality up to the specified level with least computational complexity. In this work, we propose solutions for estimating the freshness of materialized data, as one dimension of the quality, by extending cardinality estimation techniques. Experimental results show that we can estimate the freshness of materialized data with a low error rate.

Storing, Tracking, and Querying Provenance in Linked Data

The proliferation of heterogeneous Linked Data on the Web poses new challenges to database systems. In particular, the capacity to store, track, and query provenance data is becoming a pivotal feature of modern triplestores. We present methods extending a native RDF store to efficiently handle the storage, tracking, and querying of provenance in RDF data. We describe a reliable and understandable specification of the way results were derived from the data and how particular pieces of data were combined to answer a query. Subsequently, we present techniques to tailor queries with provenance data. We empirically evaluate the presented methods and show that the overhead of storing and tracking provenance is acceptable. Finally, we show that tailoring a query with provenance information can also significantly improve the performance of query execution.

Linked Data Management

The size of Linked Data is growing exponentially, thus a Linked Data management system has to be able to deal with increasing amounts of data. Additionally, in the Linked Data context, variety is especially important. In spite of its seemingly simple data model, Linked Data actually encodes rich and complex graphs mixing both instance and schema-level data. Since Linked Data is schema-free (i.e., the schema is not strict), standard databases techniques cannot be directly adopted to manage it. Even though organizing Linked Data in a form of a table is possible, querying a giant triple table becomes very costly due to the multiple nested joins required typical queries. The heterogeneity of Linked Data poses also entirely new challenges to database systems, where managing provenance information is becoming a requirement. Linked Data queries usually include multiple sources and results can be produced in various ways for a specific scenario. Such heterogeneous data can incorporate knowledge on provenance, which can be further leveraged to provide users with a reliable and understandable description of the way the query result was derived, and improve the query execution performance due to high selectivity of provenance information. In this chapter, we provide a detailed overview of current approaches specifically designed for Linked Data management. We focus on storage models, indexing techniques, and query execution strategies. Finally, we provide an overview of provenance models, definitions, and serialization techniques for Linked Data. We also survey the database management systems implementing techniques to manage provenance information in the context of Linked Data.

Linked Data for Internet of Everything

The Internet and the World Wide Web have transformed modern life by connecting the real world. With the massive connectivity extension of the Net to a much wider world wide web of the manifold physical objects, we are on the verge of the next evolution of the Internet, so called the Internet of Everything (IoE). However, enabling seamless interoperability is still a biggest challenge towards building the next generation of IoE applications. Believing in Linked Data as a promising solution to address this challenge, we propose the idea of “Linking Everything” by extending Linked Data Principles to interlink “everything” into a hypergraph. Via this book chapter, we present how to make this hypergraph programmable via the emerging Semantic Web technologies.