The complexity of SPARQL queries and RDF applications poses great challenges on distributed RDF management systems. SPARQL workloads are dynamic and con- sist of queries with variable complexities. Hence, systems that use static partitioning su↵er from communication overhead for workloads that generate excessive communi- cation. Concurrently, RDF applications are becoming more sophisticated, mandating analytical operations that extend beyond SPARQL queries. Being primarily designed and optimized to execute SPARQL queries, which lack procedural capabilities, exist- ing systems are not suitable for rich RDF analytics. This dissertation tackles the problem of accelerating SPARQL queries and RDF analytics on distributed shared-nothing RDF systems. First, a distributed RDF en- gine, coined AdPart, is introduced. AdPart uses lightweight hash partitioning for sharding triples using their subject values; rendering its startup overhead very low. The locality-aware query optimizer of AdPart takes full advantage of the partition- ing to (i) support the fully parallel processing of join patterns on subjects and (ii) minimize data communication for general queries by applying hash distribution of intermediate results instead of broadcasting, wherever possible. By exploiting hash- based locality, AdPart achieves better or comparable performance to systems that employ sophisticated partitioning schemes. To cope with workloads dynamism, AdPart is extended to dynamically adapt to workload changes. AdPart monitors the data access patterns and dynamically redis- tributes and replicates the instances of the most frequent patterns among workers.Consequently, the communication cost for future queries is drastically reduced or even eliminated. Experiments with synthetic and real data verify that AdPart starts faster than all existing systems and gracefully adapts to the query load. Finally, to support and accelerate rich RDF analytical tasks, a vertex-centric RDF analytics framework is proposed. The framework, named SPARTex, bridges the gap between RDF and graph processing. To do so, SPARTex: (i) implements a generic SPARQL operator as a vertex-centric program. The operator is coupled with an optimizer that generates e cient execution plans. (ii) It allows SPARQL to invoke vertex-centric programs as stored procedures. Finally, (iii) it provides a unified in- memory data store that allows the persistence of intermediate results. Consequently, SPARTex can e ciently support RDF analytical tasks consisting of complex pipeline of operators.

Sharing information securely between business partners and managing large supply chains effciently will be a crucial competitive advantage for enterprises in the near future. In this paper, we present a concept that allows for building value networks between business partners in a distributed manner. Companies are able to publish Linked Data which participants of the network can clone and subscribe to. Subscribers get noticed as soon as new information becomes available. This provides a technical infrastructure for business communication acts such as supply chain communication or master data management. In addition to the conceptual analysis, we provide an implementation enabling companies to create such dynamic semantic value networks.

Denna rapport innefattar utformningen av en driftmanual samt ett arbetsschema för Renova Sävenäs försorteringsanläggning. Anläggningen förbehandlar idag endast träavfall. Syftet med denna studie är att ta reda på vilka komponenter som är avgörande för att skapa ett arbetsschema och driftmanual. Det står även i fokus att se om man kan förbehandla RDF bränslet på försorteringsanläggningen. Det skall kontrolleras om det går att köra 200,500, 1000 ton/vecka igenom anläggningen. All information om driftmanual och arbetsprocess tas från gamla serviceinstruktioner och från expertis av personal på plats. Rapporten kommer även innefatta hur anläggningen fungerar under drift. Studien innehåller också en inblick i vad RDF bränsle är, hur det produceras samt hur det förbehandlas på Renova. Avslutningsvis har det också gjorts en evaluering om det finns möjlighet att förbehandla RDF på försorteringsanläggningen. Det kommer presenteras relevanta fakta baserat på skötselinstruktioner om varför det inte är angeläget att köra RDF på försorteringsanläggningen. / This report will contain the modelling and design of an operation manual and work schedule for Renova Sävenäs pre-sorting facility, the facility today is only managing the pretreatment of wood waste. The purpose of this scientific study is to further investigate what components   necessary for a complete work schedule and operation manual. The focus of this study is also to take a closer look if it is possible to switch out the wood fuel for RDF fuel. Furthermore, the amount of RDF fuel that is going to pass through the facility is 200,500, 1000 tons/week. All the information that is provided for designing the operation manual and work schedule is taken from old service instructions and the trough interviews with the personnel at the facility. This report will also contain how the facility operates during the day. Throughout this report it will be explained what RDF fuel is, how it is produced and how Renova is using it at its facility. Finally, there will be an evaluation if it's possible based on the facts presented in the report to pre-treat RDF fuel in the pre-sorting facility, and also the obstacles on why it's not feasible to do this process.

Graph queries typically involve retrieving entities with certain properties and connectivity patterns. One popular property is betweenness centrality, which is a quantitative measure of importance used in many applications such as identifying influential users in social networks. Solving graph queries that involve retrieving important entities with user-defined connectivity patterns in large graphs requires efficient com- putation of betweenness centrality and efficient graph query engines. The first part of this thesis studies the betweenness centrality problem, while the second part presents a framework for building efficient graph query engines. Computing betweenness centrality entails computing all-pairs shortest paths; thus, exact computation is costly. The performance of existing approximation algorithms is not well understood due to the lack of an established benchmark. Since graphs in many applications are inherently evolving, several incremental algorithms were proposed. However, they cannot scale to large graphs: they either require excessive memory or perform unnecessary computations rendering them prohibitively slow. Existing graph query engines rely on exhaustive indices for accelerating query evaluation. The time and memory required to build these indices can be prohibitively high for large graphs. This thesis attempts to solve the aforementioned limitations in the graph analytics literature as follows. First, we present a benchmark for evaluating betweenness centrality approximation algorithms. Our benchmark includes ground-truth data for large graphs in addition to a systematic evaluation methodology. This benchmark is the first attempt to standardize evaluating betweenness centrality approximation algorithms and it is currently being used by several research groups working on approximate between- ness in large graphs. Then, we present a linear-space parallel incremental algorithm for updating betweenness centrality in large evolving graphs. Our algorithm uses biconnected components decomposition to localize processing graph updates, and it performs incremental computation even within affected components. Our algorithm is up to an order of magnitude faster than the state-of-the-art parallel incremental algorithm. Finally, we present a framework for building low memory footprint graph query engines. Our framework avoids building exhaustive indices and uses highly optimized matrix algebra operations instead. Our framework loads datasets, and evaluates data-intensive queries up to an order of magnitude faster than existing engines.

The goal of the thesis is to study the Mozilla application platform - its structure, used technology, and the ways of development of standalone applications and extensions for the applications based on this platform (e.g. the Firefox web browser, the Thunderbird e-mail client). The thesis also contains relevant information about the used programming languages such as XUL, CSS, JavaScipt, RDF/XML and others. It describes the object oriented principles available in the JavaScript v.1.7 language. Next parts are dedicated to creating and using the platform components and the applications. The information about the platform is concluded by a presentation of the debugging and deployment possibilities. This knowledge is used to create an application able to watch changes of documents in a network environment. The thesis describes the application design and some details related to the general development of applications based on the discussed platform.

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The Semantic Web was introduced to bring structure to the Web. The goal is to allow computer agents to be able to traverse the web and carry out tasks for human users. In the Semantic Web, data is stored using the RDF data model. The purpose of this study is to explore the possibility of exposing GraphQL data to the Semantic Web using a data-to-data translation inspired by Ontology Based Data Access (OBDA). This was done by introducing GraphQL2RDF, a proof-of-concept method to materialize GraphQL data as RDF triples. GraphQL2RDF uses two mapping schemas: a GraphQL-mapping schema annotated with directives to filter and select GraphQL data and an RDF-mapping schema to specify which RDF triples to create. GraphQL2RDF supports directives for filtering based on the SQL where-clause used for filtering in SQL. The approach is demonstrated in a library use-case, in which library data exposed in a GraphQL endpoint is mapped into RDF. GraphQL2RDF demonstrates a method for exposing GraphQL data as RDF, while imposing a minimal set of requirements on the GraphQL endpoint. Future work includes improvements of the model and exploring extensions of this translation method towards an OBDA approach that does not require full materialization of the RDF data.

The growing amount of landfill waste within the EU could pose a problem in the future should there not be any effective treatment methods. This study aims to investigate the performance of landfill waste in a plasma gasification process by simulating the process in ASPEN Plus. The investigation is focused on the energy recovery potential of RDF based on composition and heating value of syngas, and cold gas efficiency (CGE). The plasma gasification system consists of a shaft gasifier and a separate tar cracking reactor where high temperature plasma is used for conversion of tar compounds considered in the model, which are toluene and naphthalene. In addition, the model is divided into five sections, namely drying, pyrolysis, char gasification, melting and tar cracking. Mass and energy balance of the system was performed to better understand the system. The results show that the plasma gasification system was able to produce a syngas with a LHV of 4.66 MJ/Nm3 while improving syngas yield by attaining a higher content of hydrogen. Thus, the plasma tar cracking of tar compounds can achieve a clean syngas and improve syngas yield. Parameter study on effect of ER show that syngas has higher heating value and CGE at lower ER. On the other hand, preheated air can help recover energy from the system while lowering the ER required for the char gasification process to meet the heat demand from partial combustion. The findings implied that landfill waste has energy potential by using a suitable treatment process such as plasma gasification.

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No description available.