Integrity Proofs for RDF Graphs

Representing open datasets with the RDF model is becoming increasingly popular. An important aspect of this data model is that it can utilize the methods of computing cryptographic hashes to verify the integrity of RDF graphs. In this paper, we first develop a number of metrics to compare the state-of-the-art integrity proof methods and then present two new approaches to generate an integrity proof of RDF datasets: (i) semantic-based and (ii) structure-based. The semantic-based approach leverages timestamps (or other inherent notions of ordering) as an indexing key to construct a sorted Merkle tree variation, where timestamps are semantically extractable from the dataset. The structure-based approach utilizes the redundant structure of large RDF datasets to compress the dataset statements prior to generating a variation of a Merkle tree. We provide a theoretical analysis and an experimental evaluation of our two proposed methods. Compared to the Merkle and sorted Merkle tree, the semantic-based approach achieves faster querying performance for large datasets. The structure-based approach is well suited when RDF datasets contain large amounts of semantic redundancies. We also evaluate our methods' resistance to adversarial threats

Compacting Frequent Star Patterns in RDF Graphs

Knowledge graphs have become a popular formalism for representing entities and their properties using a graph data model, e.g., the Resource Description Framework (RDF). An RDF graph comprises entities of the same type connected to objects or other entities using labeled edges annotated with properties. RDF graphs usually contain entities that 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 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. We address the problem of identifying frequent star patterns in RDF graphs and devise the concept of factorized RDF graphs, which denote compact representations of RDF graphs where the number of frequent star patterns is minimized. We also develop computational methods to identify frequent star patterns and generate a factorized RDF graph, where compact RDF molecules replace frequent star patterns. A compact RDF molecule of a frequent star pattern denotes an RDF subgraph that instantiates the corresponding star pattern. Instead of having all the entities matching the original frequent star pattern, a surrogate entity is added and related to the properties of the frequent star pattern; it is linked to the entities that originally match the frequent star pattern. We evaluate the performance of our factorization techniques on several RDF graph benchmarks and compare with a baseline built on top of gSpan, a state-of-the-art algorithm to detect frequent patterns. The outcomes evidence the efficiency of proposed approach and show that our techniques are able to reduce execution time of the baseline approach in at least three orders of magnitude reducing the RDF graph size by up to 66.56%

Selectivity Estimation for SPARQL Triple Patterns with Shape Expressions

International audienceShEx (Shape Expressions) is a language for expressing constraints on RDF graphs. In this work we optimize the evaluation of conjunctive SPARQL queries, on RDF graphs, by taking advantage of ShEx constraints. Our optimization is based on computing and assigning ranks to query triple patterns , dictating their order of execution. We first define a set of well formed ShEx schemas, that possess interesting characteristics for SPARQL query optimization. We then define our optimization method by exploiting information extracted from a ShEx schema. We finally report on evaluation results performed showing the advantages of applying our optimization on the top of an existing state-of-the-art query evaluation system

Optimising SPARQL Query Evaluation in the Presence of ShEx Constraints

International audienceShEx (Shape Expressions) is a language for expressing constraints on RDF graphs. In this work, we optimise the evaluation of conjunctive SPARQL queries, on RDF graphs, by taking advantage of ShEx constraints. Our optimisation is based on computing and assigning ranks to query triple patterns, dictating their order of execution. We first define a set of well-formed ShEx schemas, that possess interesting characteristics for SPARQL query optimisation. We then define our optimisation method by exploiting information extracted from a ShEx schema. We finally report on evaluation results performed showing the advantages of applying our optimisation on the top of an existing state-of-the-art query evaluation system

Effective reorganization and self-indexing of big semantic data

En esta tesis hemos analizado la redundancia estructural que los grafos RDF poseen y propuesto una técnica de preprocesamiento: RDF-Tr, que agrupa, reorganiza y recodifica los triples, tratando dos fuentes de redundancia estructural subyacentes a la naturaleza del esquema RDF. Hemos integrado RDF-Tr en HDT y k2-triples, reduciendo el tamaño que obtienen los compresores originales, superando a las técnicas más prominentes del estado del arte. Hemos denominado HDT++ y k2-triples++ al resultado de aplicar RDF-Tr en cada compresor. En el ámbito de la compresión RDF se utilizan estructuras compactas para construir autoíndices RDF, que proporcionan acceso eficiente a los datos sin descomprimirlos. HDT-FoQ es utilizado para publicar y consumir grandes colecciones de datos RDF. Hemos extendido HDT++, llamándolo iHDT++, para resolver patrones SPARQL, consumiendo menos memoria que HDT-FoQ, a la vez que acelera la resolución de la mayoría de las consultas, mejorando la relación espacio-tiempo del resto de autoíndices.Departamento de Informática (Arquitectura y Tecnología de Computadores, Ciencias de la Computación e Inteligencia Artificial, Lenguajes y Sistemas Informáticos)Doctorado en Informátic

Integrating Column-Oriented Storage and Query Processing Techniques into Graph Database Management Systems

Column-oriented RDBMSs, which support traditional read-heavy analytics workloads, employ a specific set of storage and query processing techniques for scalability and performance, such as positional tuple IDs, column-specific compression, and block-oriented processing. We revisit these techniques in the context of contemporary graph database management systems (GDBMSs). GDBMSs support a new set of analytics workloads, such as fraud detection in financial transaction networks or recommendations in social networks, that are also read-heavy but have fundamentally different access patterns than traditional analytics workloads. We first review the data characteristics and query access patterns in GDBMS to identify components of GDBMSs where existing columnar techniques can and cannot directly be used. We then present the physical data layout of columnar data structures, new columnar compression, and query-processing techniques that are optimized for GDBMSs. Our techniques include a new compact vertex and edge ID scheme, a new null and empty list compression scheme based on prefix-sums, and list-based query processing. We have integrated our techniques into GraphflowDB, an in-memory GDBMS. Compared to uncompressed storage, our compression techniques has scaled the system by 3.55x with minimal performance overheads. Our null compression scheme outperforms existing columnar schemes in query performance, with minor loss in compression rate and achieves both higher compression rate and better query performance as compared to row-oriented storage techniques adopted by existing GDBMSs. Finally, our list-based query processor techniques improve query performance by 2.7x on a variety of path queries and significantly outperform their corresponding conventional versions

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