Making Presentation Math Computable

This Open-Access-book addresses the issue of translating mathematical expressions from LaTeX to the syntax of Computer Algebra Systems (CAS). Over the past decades, especially in the domain of Sciences, Technology, Engineering, and Mathematics (STEM), LaTeX has become the de-facto standard to typeset mathematical formulae in publications. Since scientists are generally required to publish their work, LaTeX has become an integral part of today's publishing workflow. On the other hand, modern research increasingly relies on CAS to simplify, manipulate, compute, and visualize mathematics. However, existing LaTeX import functions in CAS are limited to simple arithmetic expressions and are, therefore, insufficient for most use cases. Consequently, the workflow of experimenting and publishing in the Sciences often includes time-consuming and error-prone manual conversions between presentational LaTeX and computational CAS formats. To address the lack of a reliable and comprehensive translation tool between LaTeX and CAS, this thesis makes the following three contributions. First, it provides an approach to semantically enhance LaTeX expressions with sufficient semantic information for translations into CAS syntaxes. Second, it demonstrates the first context-aware LaTeX to CAS translation framework LaCASt. Third, the thesis provides a novel approach to evaluate the performance for LaTeX to CAS translations on large-scaled datasets with an automatic verification of equations in digital mathematical libraries. This is an open access book

Compact semantic representations of observational data

Das Konzept des Internet der Dinge (IoT) ist in mehreren Bereichen weit verbreitet, damit Geräte miteinander interagieren und bestimmte Aufgaben erfüllen können. IoT-Geräte umfassen verschiedene Konzepte, z.B. Sensoren, Programme, Computer und Aktoren. IoT-Geräte beobachten ihre Umgebung, um Informationen zu sammeln und miteinander zu kommunizieren, um gemeinsame Aufgaben zu erfüllen. Diese Vorrichtungen erzeugen kontinuierlich Beobachtungsdatenströme, die zu historischen Daten werden, wenn diese Beobachtungen gespeichert werden. Durch die Zunahme der Anzahl der IoT-Geräte wird eine große Menge an Streaming- und historischen Beobachtungsdaten erzeugt. Darüber hinaus wurden mehrere Ontologien, wie die Semantic Sensor Network (SSN) Ontologie, für die semantische Annotation von Beobachtungsdaten vorgeschlagen - entweder Stream oder historisch. Das Resource Description Framework (RDF) ist ein weit verbreitetes Datenmodell zur semantischen Beschreibung der Datensätze. Semantische Annotation bietet ein gemeinsames Verständnis für die Verarbeitung und Analyse von Beobachtungsdaten. Durch das Hinzufügen von Semantik wird die Datengröße jedoch weiter erhöht, insbesondere wenn die Beobachtungswerte von mehreren Geräten redundant erfasst werden. So können beispielsweise mehrere Sensoren Beobachtungen erzeugen, die den gleichen Wert für die relative Luftfeuchtigkeit in einem bestimmten Zeitstempel und einer bestimmten Stadt anzeigen. Diese Situation kann in einem RDF-Diagramm mit vier RDF-Tripel dargestellt werden, wobei Beobachtungen als Tripel dargestellt werden, die das beobachtete Phänomen, die Maßeinheit, den Zeitstempel und die Koordinaten beschreiben. Die RDF-Tripel einer Beobachtung sind mit dem gleichen Thema verbunden. Solche Beobachtungen teilen sich die gleichen Objekte in einer bestimmten Gruppe von Eigenschaften, d.h. sie entsprechen einem Sternmuster, das sich aus diesen Eigenschaften und Objekten zusammensetzt. Wenn die Anzahl dieser Subjektentitäten oder Eigenschaften in diesen Sternmustern groß ist, wird die Größe des RDF-Diagramms und der Abfrageverarbeitung negativ beeinflusst; wir bezeichnen diese Sternmuster als häufige Sternmuster. Diese Arbeit befasst sich mit dem Problem der Identifizierung von häufigen Sternenmustern in RDF-Diagrammen und entwickelt Berechnungsmethoden, um häufige Sternmuster zu identifizieren und ein faktorisiertes RDF-Diagramm zu erzeugen, bei dem die Anzahl der häufigen Sternmuster minimiert wird. Darüber hinaus wenden wir diese faktorisierten RDF-Darstellungen über historische semantische Sensordaten an, die mit der SSN-Ontologie beschrieben werden, und präsentieren tabellarische Darstellungen von faktorisierten semantischen Sensordaten, um Big Data-Frameworks auszunutzen. Darüber hinaus entwickelt diese Arbeit einen wissensbasierten Ansatz namens DESERT, der in der Lage ist, bei Bedarf Streamdaten zu faktorisieren und semantisch anzureichern (on-Demand factorizE and Semantically Enrich stReam daTa). Wir bewerten die Leistung unserer vorgeschlagenen Techniken anhand mehrerer RDF-Diagramm-Benchmarks. Die Ergebnisse zeigen, dass unsere Techniken in der Lage sind, häufige Sternmuster effektiv und effizient zu erkennen, und die Größe der RDF-Diagramme kann um bis zu 66,56% reduziert werden, während die im ursprünglichen RDF-Diagramm dargestellten Daten erhalten bleiben. Darüber hinaus sind die kompakten Darstellungen in der Lage, die Anzahl der RDF-Tripel um mindestens 53,25% in historischen Beobachtungsdaten und bis zu 94,34% in Beobachtungsdatenströmen zu reduzieren. Darüber hinaus reduzieren die Ergebnisse der Anfrageauswertung über historische Daten die Ausführungszeit der Anfrage um bis zu drei Größenordnungen. In Beobachtungsdatenströmen wird die Größe der zur Beantwortung der Anfrage benötigten Daten um 92,53% reduziert, wodurch der Speicherplatzbedarf zur Beantwortung der Anfragen reduziert wird. Diese Ergebnisse belegen, dass IoT-Daten mit den vorgeschlagenen kompakten Darstellungen effizient dargestellt werden können, wodurch die negativen Auswirkungen semantischer Annotationen auf das IoT-Datenmanagement reduziert werden.The Internet of Things (IoT) concept has been widely adopted in several domains to enable devices to interact with each other and perform certain tasks. IoT devices encompass different concepts, e.g., sensors, programs, computers, and actuators. IoT devices observe their surroundings to collect information and communicate with each other in order to perform mutual tasks. These devices continuously generate observational data streams, which become historical data when these observations are stored. Due to an increase in the number of IoT devices, a large amount of streaming and historical observational data is being produced. Moreover, several ontologies, like the Semantic Sensor Network (SSN) Ontology, have been proposed for semantic annotation of observational data-either streams or historical. Resource Description Framework (RDF) is widely adopted data model to semantically describe the datasets. Semantic annotation provides a shared understanding for processing and analysis of observational data. However, adding semantics, further increases the data size especially when the observation values are redundantly sensed by several devices. For example, several sensors can generate observations indicating the same value for relative humidity in a given timestamp and city. This situation can be represented in an RDF graph using four RDF triples where observations are represented as triples that describe the observed phenomenon, the unit of measurement, the timestamp, and the coordinates. The RDF triples of an observation are associated with the same subject. Such observations share the same objects in a certain group of properties, i.e., they match star patterns composed of these properties and objects. In case the number of these subject entities or properties in these star patterns is large, the size of the RDF graph and query processing are negatively impacted; we refer these star patterns as frequent star patterns. This thesis addresses the problem of identifying frequent star patterns in RDF graphs and develop computational methods to identify frequent star patterns and generate a factorized RDF graph where the number of frequent star patterns is minimized. Furthermore, we apply these factorized RDF representations over historical semantic sensor data described using the SSN ontology and present tabular-based representations of factorized semantic sensor data in order to exploit Big Data frameworks. In addition, this thesis devises a knowledge-driven approach named DESERT that is able to on-Demand factorizE and Semantically Enrich stReam daTa. We evaluate the performance of our proposed techniques on several RDF graph benchmarks. The outcomes show that our techniques are able to effectively and efficiently detect frequent star patterns and RDF graph size can be reduced by up to 66.56% while data represented in the original RDF graph is preserved. Moreover, the compact representations are able to reduce the number of RDF triples by at least 53.25% in historical observational data and upto 94.34% in observational data streams. Additionally, query evaluation results over historical data reduce query execution time by up to three orders of magnitude. In observational data streams the size of the data required to answer the query is reduced by 92.53% reducing the memory space requirements to answer the queries. These results provide evidence that IoT data can be efficiently represented using the proposed compact representations, reducing thus, the negative impact that semantic annotations may have on IoT data management

The Data Value Quest: A Holistic Semantic Approach at Bosch

Modern industry witnesses a fast growth in volume and complexity of heterogeneous manufacturing (big) data thanks to the technological advances of Industry 4.0, including development in perception, communication, processing, and actuation. Data has become the new oil for industries. However, despite the effort and time invested in the data business, there still exists a big room for improvement in exploiting the value of data. In particular, data is still often scattered and stored in silos affecting its usage; a lot of data generated by sensors is not used in applications; companies possess precious data but do not have a trustworthy scheme to share its value; etc. There are certainly many ways to address these issues. In this paper we discuss the dimension of meaning in data and how we address it at Bosch in a holistic semantification fashion that bestows data with meanings which has always been important for humans to perceive, comprehend, reason, and produce. We believe the emphasis, the clarification, and the promotion of the eminent and profound roles of semantic technologies in the industry should lead to considerable opportunities for advances in technology, growth of profitability, and paradigm change in the industrial practice

Making Presentation Math Computable

This Open-Access-book addresses the issue of translating mathematical expressions from LaTeX to the syntax of Computer Algebra Systems (CAS). Over the past decades, especially in the domain of Sciences, Technology, Engineering, and Mathematics (STEM), LaTeX has become the de-facto standard to typeset mathematical formulae in publications. Since scientists are generally required to publish their work, LaTeX has become an integral part of today's publishing workflow. On the other hand, modern research increasingly relies on CAS to simplify, manipulate, compute, and visualize mathematics. However, existing LaTeX import functions in CAS are limited to simple arithmetic expressions and are, therefore, insufficient for most use cases. Consequently, the workflow of experimenting and publishing in the Sciences often includes time-consuming and error-prone manual conversions between presentational LaTeX and computational CAS formats. To address the lack of a reliable and comprehensive translation tool between LaTeX and CAS, this thesis makes the following three contributions. First, it provides an approach to semantically enhance LaTeX expressions with sufficient semantic information for translations into CAS syntaxes. Second, it demonstrates the first context-aware LaTeX to CAS translation framework LaCASt. Third, the thesis provides a novel approach to evaluate the performance for LaTeX to CAS translations on large-scaled datasets with an automatic verification of equations in digital mathematical libraries. This is an open access book

Open Government Data: Fostering Innovation

The provision of public information contributes to the enrichment and enhancement of the data produced by the government as part of its activities, and the transformation of heterogeneous data into information and knowledge. This process of opening changes the operational mode of public administrations, leveraging the data management, encouraging savings and especially in promoting the development of services in subsidiary and collaborative form between public and private entities. The demand for new services also promotes renewed entrepreneurship centred on responding to new social and territorial needs through new technologies. In this sense we speak of Open Data as an enabling infrastructure for the development of innovation and as an instrument to the development and diffusion of Innovation and Communications Technology (ICT) in the public system as well as creating space for innovation for businesses, particularly SMEs, based on the exploitation of information assets of the territory. The Open Data Trentino Project has initiated and fosters the process of opening of public information and develops as a natural consequence of this process of openness, the creation of innovative services for and with the citizens. In this paper we present how our project acts on long-chain, from raw data till reusable meaningful and scalable knowledge base that leads to the production of data reuse through the implementation of services that will enhance and transform the data into information capable of responding to specific questions efficiency and innovation

The potential of semantic paradigm in warehousing of big data

Big data have analytical potential that was hard to realize with available technologies. After new storage paradigms intended for big data such as NoSQL databases emerged, traditional systems got pushed out of the focus. The current research is focused on their reconciliation on different levels or paradigm replacement. Similarly, the emergence of NoSQL databases has started to push traditional (relational) data warehouses out of the research and even practical focus. Data warehousing is known for the strict modelling process, capturing the essence of the business processes. For that reason, a mere integration to bridge the NoSQL gap is not enough. It is necessary to deal with this issue on a higher abstraction level during the modelling phase. NoSQL databases generally lack clear, unambiguous schema, making the comprehension of their contents difficult and their integration and analysis harder. This motivated involving semantic web technologies to enrich NoSQL database contents by additional meaning and context. This paper reviews the application of semantics in data integration and data warehousing and analyses its potential in integrating NoSQL data and traditional data warehouses with some focus on document stores. Also, it gives a proposal of the future pursuit directions for the big data warehouse modelling phases

Chemical information matters: an e-Research perspective on information and data sharing in the chemical sciences

Recently, a number of organisations have called for open access to scientific information and especially to the data obtained from publicly funded research, among which the Royal Society report and the European Commission press release are particularly notable. It has long been accepted that building research on the foundations laid by other scientists is both effective and efficient. Regrettably, some disciplines, chemistry being one, have been slow to recognise the value of sharing and have thus been reluctant to curate their data and information in preparation for exchanging it. The very significant increases in both the volume and the complexity of the datasets produced has encouraged the expansion of e-Research, and stimulated the development of methodologies for managing, organising, and analysing "big data". We review the evolution of cheminformatics, the amalgam of chemistry, computer science, and information technology, and assess the wider e-Science and e-Research perspective. Chemical information does matter, as do matters of communicating data and collaborating with data. For chemistry, unique identifiers, structure representations, and property descriptors are essential to the activities of sharing and exchange. Open science entails the sharing of more than mere facts: for example, the publication of negative outcomes can facilitate better understanding of which synthetic routes to choose, an aspiration of the Dial-a-Molecule Grand Challenge. The protagonists of open notebook science go even further and exchange their thoughts and plans. We consider the concepts of preservation, curation, provenance, discovery, and access in the context of the research lifecycle, and then focus on the role of metadata, particularly the ontologies on which the emerging chemical Semantic Web will depend. Among our conclusions, we present our choice of the "grand challenges" for the preservation and sharing of chemical information

Optimizing Federated Queries Based on the Physical Design of a Data Lake

The optimization of query execution plans is known to be crucial for reducing the query execution time. In particular, query optimization has been studied thoroughly for relational databases over the past decades. Recently, the Resource Description Framework (RDF) became popular for publishing data on the Web. As a consequence, federations composed of different data models like RDF and relational databases evolved. One type of these federations are Semantic Data Lakes where every data source is kept in its original data model and semantically annotated with ontologies or controlled vocabularies. However, state-of-the-art query engines for federated query processing over Semantic Data Lakes often rely on optimization techniques tailored for RDF. In this paper, we present query optimization techniques guided by heuristics that take the physical design of a Data Lake into account. The heuristics are implemented on top of Ontario, a SPARQL query engine for Semantic Data Lakes. Using sourcespecific heuristics, the query engine is able to generate more efficient query execution plans by exploiting the knowledge about indexes and normalization in relational databases. We show that heuristics which take the physical design of the Data Lake into account are able to speed up query processing

Toward Representing Research Contributions in Scholarly Knowledge Graphs Using Knowledge Graph Cells

There is currently a gap between the natural language expression of scholarly publications and their structured semantic content modeling to enable intelligent content search. With the volume of research growing exponentially every year, a search feature operating over semantically structured content is compelling. Toward this end, in this work, we propose a novel semantic data model for modeling the contribution of scientific investigations. Our model, i.e. the Research Contribution Model (RCM), includes a schema of pertinent concepts highlighting six core information units, viz. Objective, Method, Activity, Agent, Material, and Result, on which the contribution hinges. It comprises bottom-up design considerations made from three scientific domains, viz. Medicine, Computer Science, and Agriculture, which we highlight as case studies. For its implementation in a knowledge graph application we introduce the idea of building blocks called Knowledge Graph Cells (KGC), which provide the following characteristics: (1) they limit the expressibility of ontologies to what is relevant in a knowledge graph regarding specific concepts on the theme of research contributions; (2) they are expressible via ABox and TBox expressions; (3) they enforce a certain level of data consistency by ensuring that a uniform modeling scheme is followed through rules and input controls; (4) they organize the knowledge graph into named graphs; (5) they provide information for the front end for displaying the knowledge graph in a human-readable form such as HTML pages; and (6) they can be seamlessly integrated into any existing publishing process thatsupports form-based input abstracting its semantic technicalities including RDF semantification from the user. Thus RCM joins the trend of existing work toward enhanced digitalization of scholarly publication enabled by an RDF semantification as a knowledge graph fostering the evolution of the scholarly publications beyond written text

DARE: A Reflective Platform Designed to Enable Agile Data-Driven Research on the Cloud

The DARE platform has been designed to help research developers deliver user-facing applications and solutions over diverse underlying e-infrastructures, data and computational contexts. The platform is Cloud-ready, and relies on the exposure of APIs, which are suitable for raising the abstraction level and hiding complexity. At its core, the platform implements the cataloguing and execution of fine-grained and Python-based dispel4py workflows as services. Reflection is achieved via a logical knowledge base, comprising multiple internal catalogues, registries and semantics, while it supports persistent and pervasive data provenance. This paper presents design and implementation aspects of the DARE platform, as well as it provides directions for future development.PublishedSan Diego (CA, USA)3IT. Calcolo scientific