A Selectivity based approach to Continuous Pattern Detection in Streaming Graphs

Cyber security is one of the most significant technical challenges in current times. Detecting adversarial activities, prevention of theft of intellectual properties and customer data is a high priority for corporations and government agencies around the world. Cyber defenders need to analyze massive-scale, high-resolution network flows to identify, categorize, and mitigate attacks involving networks spanning institutional and national boundaries. Many of the cyber attacks can be described as subgraph patterns, with prominent examples being insider infiltrations (path queries), denial of service (parallel paths) and malicious spreads (tree queries). This motivates us to explore subgraph matching on streaming graphs in a continuous setting. The novelty of our work lies in using the subgraph distributional statistics collected from the streaming graph to determine the query processing strategy. We introduce a "Lazy Search" algorithm where the search strategy is decided on a vertex-to-vertex basis depending on the likelihood of a match in the vertex neighborhood. We also propose a metric named "Relative Selectivity" that is used to select between different query processing strategies. Our experiments performed on real online news, network traffic stream and a synthetic social network benchmark demonstrate 10-100x speedups over selectivity agnostic approaches.Comment: in 18th International Conference on Extending Database Technology (EDBT) (2015

Framework for Live Synchronization of RDF Views of Relational Data

This Demo presents a framework for the live synchronization of an RDF view defined on top of relational database. In the proposed framework, rules are responsible for computing and publishing the changeset required for the RDB-RDF view to stay synchronized with the relational database. The computed changesets are then used for the incremental maintenance of the RDB_RDF views as well as application views. The Demo is based on the LinkedBrainz Live tool, developed to validate the proposed framework

vSPARQL: A View Definition Language for the Semantic Web

Translational medicine applications would like to leverage the biological and biomedical ontologies, vocabularies, and data sets available on the semantic web. We present a general solution for RDF information set reuse inspired by database views. Our view definition language, vSPARQL, allows applications to specify the exact content that they are interested in and how that content should be restructured or modified. Applications can access relevant content by querying against these view definitions. We evaluate the expressivity of our approach by defining views for practical use cases and comparing our view definition language to existing query languages

Migrating microservices to graph database

Microservice architecture is a popular approach to structuring web backend services. Another emerging trend, after a period of hibernation, is utilizing modern graph database management systems for managing complex, richly connected data. The two approaches have rarely been used in tandem, as microservices emphasize modularization and decoupling of services, while graph data models favor data integration. In this study, literature on microservices and graph databases is reviewed and a synthesis between the two paradigms is presented. Based on the theoretical discussion, a software architecture combining the two elements is formulated and implemented using microservices serving content metadata at Yleisradio, the Finnish national broadcasting company. The architecture design follows the Design Science Research Process model. Finally, the renewed system is evaluated using quantitative and qualitative metrics. The performance of the system is measured using automated API queries and load tests. The new system was compared to an earlier version based on a PostgreSQL database. The tests gave slight indication that the renewed system performed better for complex queries, where a large number of relations were traversed, but worse in terms of throughput under heavy load. Based on the these findings, a number of performance-enhancing optimizations to the system are introduced. Observations and perpectives are also gathered in a project retrospective session. It is concluded that the resulting architecture holds promise for managing complex data rich in relations in a safe manner. In it, the different domains of the knowledge graph are decoupled into distinct named graphs managed by different microservices

i3MAGE: Incremental, Interactive, Inter-Model Mapping Generation

Data integration is a highly important prerequisite for most enterprise data analyses. While hard in general, a particular concern is about human effort for designing a global integration schema, authoring queries against that schema, and creating mappings to connect data sources with the global schema. Ontology-based data integration (OBDI), which employs ontologies as a target model, reduces the effort for schema design and usage. On the other side, it requires mappings that are particularly difficult to create. Architects who work with OBDI hence need systems to support the process of mapping development. One key type of tooling to support mapping development is automatic or semi-automatic generation of mapping suggestions. While many such tools exist in the wider sphere of data integration, few are built to work in the case of OBDI, where the inter-model gap between relational input schemata and a target ontology has to be bridged. Among those that support OBDI at all, none so far are fully optimized for this specific case by performing a truly inter-model matching while also leveraging distinct but corresponding aspects of both models. We propose i3MAGE, an approach and a system for automatic and semi-automatic generation of mappings in OBDI. The system is built on generic inter-model matching, and it is optimized in various ways for matching relational source schemata to target ontology schemata. To be truly semi-automatic in every respect, i3MAGE works both incrementally, building mappings pay-as-you-go, and interactively in exchange with a human user. We introduce a specialized benchmark and evaluate i3MAGE against a number of other approaches. In addition, we provide examples, where i3MAGE can be deployed in holistic data integration environments

Connected Information Management

Society is currently inundated with more information than ever, making efficient management a necessity. Alas, most of current information management suffers from several levels of disconnectedness: Applications partition data into segregated islands, small notes don’t fit into traditional application categories, navigating the data is different for each kind of data; data is either available at a certain computer or only online, but rarely both. Connected information management (CoIM) is an approach to information management that avoids these ways of disconnectedness. The core idea of CoIM is to keep all information in a central repository, with generic means for organization such as tagging. The heterogeneity of data is taken into account by offering specialized editors. The central repository eliminates the islands of application-specific data and is formally grounded by a CoIM model. The foundation for structured data is an RDF repository. The RDF editing meta-model (REMM) enables form-based editing of this data, similar to database applications such as MS access. Further kinds of data are supported by extending RDF, as follows. Wiki text is stored as RDF and can both contain structured text and be combined with structured data. Files are also supported by the CoIM model and are kept externally. Notes can be quickly captured and annotated with meta-data. Generic means for organization and navigation apply to all kinds of data. Ubiquitous availability of data is ensured via two CoIM implementations, the web application HYENA/Web and the desktop application HYENA/Eclipse. All data can be synchronized between these applications. The applications were used to validate the CoIM ideas

S-RDF: A New RDF Serialization Format for Better Storage Without Losing Human Readability

International audienceNowadays, RDF data becomes more and more popular on the Web due to the advances of the Semantic Web and the Linked Open Data initiatives. Several works are focused on transforming relational databases to RDF by storing related data in N-Triple serialization format. However, these approaches do not take into account the existing normalization of their databases since N-Triple format allows data redundancy and does not control any normalization by itself. Moreover, the mostly used and recommended serialization formats, such as RDF/XML, Turtle, and HDT, have either high human-readability but waste storage capacity, or focus further on storage capacities while providing low human-readability. To overcome these limitations, we propose here a new serialization format, called S-RDF. By considering the structure (graph) and values of the RDF data separately, S-RDF reduces the duplicity of values by using unique identifiers. Results show an important improvement over the existing serialization formats in terms of storage (up to 71,66% w.r.t. N-Triples) and human readability