BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Pentabarf//Schedule 0.3//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALDESC;VALUE=TEXT:Graph Processing devroom X-WR-CALNAME;VALUE=TEXT:Graph Processing devroom X-WR-TIMEZONE;VALUE=TEXT:Europe/Brussels BEGIN:VEVENT METHOD:PUBLISH UID:7104@FOSDEM18@fosdem.org TZID:Europe-Brussels DTSTART:20180203T103000 DTEND:20180203T110500 SUMMARY:Cypher: An evolving query language for property graphs DESCRIPTION:
Cypher is a property graph query language that provides expressive and efficient querying of graph data. Originally designed and implemented within the Neo4j graph database, it is now being used by several industrial database products, as well as open-source and research projects.
CLASS:PUBLIC STATUS:CONFIRMED CATEGORIES:Graph Processing URL:https:/fosdem.org/2018/schedule/2018/schedule/event/cypher_evolving_query_language/ LOCATION:H.2214 ATTENDEE;ROLE=REQ-PARTICIPANT;CUTYPE=INDIVIDUAL;CN="Stefan Plantikow":invalid:nomail END:VEVENT BEGIN:VEVENT METHOD:PUBLISH UID:6866@FOSDEM18@fosdem.org TZID:Europe-Brussels DTSTART:20180203T111500 DTEND:20180203T115000 SUMMARY:Cypher for Apache Spark DESCRIPTION:Graph pattern matching is one of the most interesting and challenging operations in graph analytics. Query languages like openCypher, implemented in systems like Neo4j, SAP HANA Graph and Redis Graph, allow the intuitive definition of graph patterns including structural and semantic predicates.
For now, graph query languages are most prominent in graph database systems such as Neo4j. However, we think that many systems can benefit from having such a language in their toolbox. One of these systems is Apache Spark, which is one of the most popular open source frameworks in the context of distributed processing of large data volumes within complex analytical workloads.To bring the benefits of Cypher from the graph database realm into the world of Big Data, we at Neo4j started developing Cypher for Apache Spark (CAPS). CAPS is primarily focused on graph-powered data integration and graph analytical query workloads within the Spark ecosystem. In addition, CAPS is our testbed for Cypher language extensions as specified in the openCypher project; for example, multiple graphs, graph transformations and construction, and query composition.
In our talk, we want to motivate use-cases for CAPS and give an overview of new querying capabilities which we demonstrate using Apache Spark and Apache Zeppelin. Furthermore, we briefly present the internal architecture highlighting the main differences between Neo4j and CAPS.
CLASS:PUBLIC STATUS:CONFIRMED CATEGORIES:Graph Processing URL:https:/fosdem.org/2018/schedule/2018/schedule/event/cypher_for_apache_spark/ LOCATION:H.2214 ATTENDEE;ROLE=REQ-PARTICIPANT;CUTYPE=INDIVIDUAL;CN="Martin Junghanns":invalid:nomail ATTENDEE;ROLE=REQ-PARTICIPANT;CUTYPE=INDIVIDUAL;CN="Max Kießling":invalid:nomail END:VEVENT BEGIN:VEVENT METHOD:PUBLISH UID:6846@FOSDEM18@fosdem.org TZID:Europe-Brussels DTSTART:20180203T120000 DTEND:20180203T123500 SUMMARY:The Computer Science behind a modern distributed data store DESCRIPTION:What we see in the modern data store world is a race between different approaches to achieve a distributed and resilient storage of data. Most applications need a stateful layer which holds the data. There are at least three necessary ingredients which are everything else than trivial to combine and of course even more challenging when heading for an acceptable performance.
Over the past years there has been significant progress in respect in both the science and practical implementations of such data stores. In his talk Max Neunhoeffer will introduce the audience to some of the needed ingredients, address the difficulties of their interplay and show four modern approaches of distributed open-source data stores.
CLASS:PUBLIC STATUS:CONFIRMED CATEGORIES:Graph Processing URL:https:/fosdem.org/2018/schedule/2018/schedule/event/computer_science_of_modern_distributed_database/ LOCATION:H.2214 ATTENDEE;ROLE=REQ-PARTICIPANT;CUTYPE=INDIVIDUAL;CN="Michael Hackstein":invalid:nomail END:VEVENT BEGIN:VEVENT METHOD:PUBLISH UID:6564@FOSDEM18@fosdem.org TZID:Europe-Brussels DTSTART:20180203T124500 DTEND:20180203T132000 SUMMARY:Analzying Blockchain transactions in Apache Spark DESCRIPTION:I will show the Blockchain analysis in Jupyter interactive notebook using the external Spark cluster running in Kubernetes, everything dockerized.
The talk will briefly describe how Blockchain transactions work, but most of the time would be the demo. In the demo I will show how we can run various queries on the publicly available Blockchain data, graph algorithms such as PageRank for identifying significant BTC addresses and more.
Intended audience: intermediate, analysts, Bitcoin/Altcoin enthusiasts
CLASS:PUBLIC STATUS:CONFIRMED CATEGORIES:Graph Processing URL:https:/fosdem.org/2018/schedule/2018/schedule/event/analzying_blockchain_transactions_in_apache_spark/ LOCATION:H.2214 ATTENDEE;ROLE=REQ-PARTICIPANT;CUTYPE=INDIVIDUAL;CN="Jiří Kremser":invalid:nomail END:VEVENT BEGIN:VEVENT METHOD:PUBLISH UID:6648@FOSDEM18@fosdem.org TZID:Europe-Brussels DTSTART:20180203T133000 DTEND:20180203T140500 SUMMARY:G-CORE: The LDBC Graph Query Language Proposal DESCRIPTION:The talk will present G-CORE and its most striking features. G-CORE is a graph query language proposal developed by Linked Data Benchmark Council (LDBC). It is a closed graph query language that includes graph pattern matching, inclusive extended regular path expressions, graph construction (graph projection and graph aggregation), and (weighted) shortest path finding. The query language queries graphs and returns a graph. Hence, it supports views and correlated sub-queries, which have proven their worth in the relational databases. To ensure that the query language is practically usable on large data, G-CORE builds on previous complexity results and carefully select useful features, but restricted in such ways that the resulting language is polynomial in data complexity. As such, G-CORE connects the practical work done in industrial proposals with the foundational research on graph databases; in fact, it can be considered as a bridge between these two worlds.
CLASS:PUBLIC STATUS:CONFIRMED CATEGORIES:Graph Processing URL:https:/fosdem.org/2018/schedule/2018/schedule/event/gcore_query_language/ LOCATION:H.2214 ATTENDEE;ROLE=REQ-PARTICIPANT;CUTYPE=INDIVIDUAL;CN="Hannes Voigt":invalid:nomail END:VEVENT BEGIN:VEVENT METHOD:PUBLISH UID:7216@FOSDEM18@fosdem.org TZID:Europe-Brussels DTSTART:20180203T141500 DTEND:20180203T145000 SUMMARY:Efficient Graph Algorithms in Neo4j DESCRIPTION:We recently released a graph algorithms library for Neo4j.
You can use these graph algorithms on your connected data to gain new insights more easily within the transactional database and to improve query results from your graph data, for example by focusing on particular communities or favoring popular entities.
We developed this library as part of our effort to make it easier to use Neo4j for a wider variety of applications. Many users expressed interest in running graph algorithms directly on Neo4j without having to employ a secondary system.
We also tuned these algorithms to be as efficient as possible in regards to resource utilization as well as streamlined for later management and debugging.
In this session, we'll look at some of these graph algorithms and the types of problems that you can use them for in your applications and the implementation choices we made.
CLASS:PUBLIC STATUS:CONFIRMED CATEGORIES:Graph Processing URL:https:/fosdem.org/2018/schedule/2018/schedule/event/efficient_graph_algorithms_neo4j/ LOCATION:H.2214 ATTENDEE;ROLE=REQ-PARTICIPANT;CUTYPE=INDIVIDUAL;CN="Michael Hunger":invalid:nomail END:VEVENT BEGIN:VEVENT METHOD:PUBLISH UID:6845@FOSDEM18@fosdem.org TZID:Europe-Brussels DTSTART:20180203T150000 DTEND:20180203T153500 SUMMARY:Handling Billions Of Edges in a Graph Database DESCRIPTION:The complexity and amount of data rises. Modern graph databases are designed to handle the complexity but still not for the amount of data. When hitting a certain size of a graph, many dedicated graph databases reach their limits in vertical or, most common, horizontal scalability. In this talk I'll provide a brief overview about current approaches and their limits towards scalability. Dealing with complex data in a complex system doesn't make things easier... but more fun finding a solution. Join me on my journey to handle billions of edges in a graph database.
CLASS:PUBLIC STATUS:CONFIRMED CATEGORIES:Graph Processing URL:https:/fosdem.org/2018/schedule/2018/schedule/event/handling_billion_edges__in_graphdb/ LOCATION:H.2214 ATTENDEE;ROLE=REQ-PARTICIPANT;CUTYPE=INDIVIDUAL;CN="Michael Hackstein":invalid:nomail END:VEVENT BEGIN:VEVENT METHOD:PUBLISH UID:6445@FOSDEM18@fosdem.org TZID:Europe-Brussels DTSTART:20180203T154500 DTEND:20180203T162000 SUMMARY:It's a Trie... it's a Graph... it's a Traph! DESCRIPTION: CLASS:PUBLIC STATUS:CONFIRMED CATEGORIES:Graph Processing URL:https:/fosdem.org/2018/schedule/2018/schedule/event/multi_level_graph_index/ LOCATION:H.2214 ATTENDEE;ROLE=REQ-PARTICIPANT;CUTYPE=INDIVIDUAL;CN="Guillaume Plique":invalid:nomail END:VEVENT BEGIN:VEVENT METHOD:PUBLISH UID:6719@FOSDEM18@fosdem.org TZID:Europe-Brussels DTSTART:20180203T163000 DTEND:20180203T170500 SUMMARY:Graph-based analysis of JavaScript source code repositories DESCRIPTION:JavaScript is one of the decade’s most trending languages. It ranked #1 in popularity in Stack Overflow questions and is consistently featured in the top 10 languages of the TIOBE Index. Originally intended for client-side scripting, the language is now widely used to build complex desktop applications, write server-side code and program IoT devices. The latest standards of the language are released yearly under the ECMAScript trademark and contain sophisticated features and syntactical constructs.
Static analysis is a software testing approach that is performed without compiling and executing the program itself. This allows developers to catch programming errors before building, testing and deploying the code. There is a wide range of static analysis tools: linters and code style analysers repeatedly perform checks in IDEs, while more complex analyzers, such as type checkers, run as part of the continuous integration (CI) process.
As JavaScript is a dynamic language, static analysis approaches are particularly useful: they can detect erroneous type usages that would not be revealed by building the code, but only occur during thorough testing or even worse, at production. Thanks to the popularity of the language, there are already numerous approaches for static analysis available, such as Tern, Facebook’s Flow and TAJS. However, none of these fitted all of our requirements:
As none of the current approaches satisfied these requirements, we built our own solution that uses a property graph query engine to represent the code graphs used for analysis and graph queries to evaluate the analysis rules. Compared to other static analysis frameworks, the novelty of our solution is twofold:
Using declarative queries, our tool is able to perform the complex analysis queries quickly, including:
The analysis can be easily extended by custom analysis rules defined in the openCypher language. Building the system on openCypher also allows us to use different query engines: both mature databases, such as Neo4j, and also experimental engines, such as our own ingraph engine. The latter is our research prototype that supports live query evaluation for Cypher queries, which allows near instant answers even for complex analysis rules.
In this talk, we give an overview of the steps involved in transforming the source code file to a syntax graph and converting it to a call flow graph. We demonstrate how openCypher queries can be used to capture complex analysis rules in a concise way, and how ingraph allows us to continuously evaluate these queries.
CLASS:PUBLIC STATUS:CONFIRMED CATEGORIES:Graph Processing URL:https:/fosdem.org/2018/schedule/2018/schedule/event/graph_based_analysis_javascript_repos/ LOCATION:H.2214 ATTENDEE;ROLE=REQ-PARTICIPANT;CUTYPE=INDIVIDUAL;CN="Gabor Szarnyas":invalid:nomail END:VEVENT BEGIN:VEVENT METHOD:PUBLISH UID:7341@FOSDEM18@fosdem.org TZID:Europe-Brussels DTSTART:20180203T171500 DTEND:20180203T173000 SUMMARY:Etienne Saliez - A look at “Natural Intelligence” DESCRIPTION:Up to now many medical record softwares were not mach more than narrative reports of what did appen at some time in the past in some specific context.Student can find much static knowledge on internet, but have to learn a “medical methodology” about how to use this large amount of knowledge.
CLASS:PUBLIC STATUS:CONFIRMED CATEGORIES:Graph Processing URL:https:/fosdem.org/2018/schedule/2018/schedule/event/graphdevroom_immm/ LOCATION:H.2214 ATTENDEE;ROLE=REQ-PARTICIPANT;CUTYPE=INDIVIDUAL;CN="Michael Hunger":invalid:nomail END:VEVENT END:VCALENDAR