BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Pentabarf//Schedule 0.3//EN CALSCALE:GREGORIAN METHOD:PUBLISH X-WR-CALDESC;VALUE=TEXT:Graph devroom X-WR-CALNAME;VALUE=TEXT:Graph devroom X-WR-TIMEZONE;VALUE=TEXT:Europe/Brussels BEGIN:VEVENT METHOD:PUBLISH UID:5199@FOSDEM17@fosdem.org TZID:Europe-Brussels DTSTART:20170204T103000 DTEND:20170204T110000 SUMMARY:Intro to Graph databases DESCRIPTION:
An introduction to graph databases.From graph theory, through the history of computing and how it affected database design, to why relational databases aren't about relations.Next, a look how diverse is the current graph database market and what obvious and not so obvious problems are solved by graphs.A short introduction to Neo4j's query language, Cypher, will show the main concepts of querying graph data. Then, by use of the same datasets in both relational and graph databases will compare syntax clarity and database performance.
CLASS:PUBLIC STATUS:CONFIRMED CATEGORIES:Graph URL:https:/fosdem.org/2017/schedule/2017/schedule/event/graph_intro_graph_databases/ LOCATION:H.2214 ATTENDEE;ROLE=REQ-PARTICIPANT;CUTYPE=INDIVIDUAL;CN="Szymon Warda":invalid:nomail END:VEVENT BEGIN:VEVENT METHOD:PUBLISH UID:5546@FOSDEM17@fosdem.org TZID:Europe-Brussels DTSTART:20170204T111000 DTEND:20170204T114000 SUMMARY:Using graph databases in popular open source CMSs DESCRIPTION:Traditionally CMSs use SQL databases that are really fast when you need all the information stored together in a record row, but are a bad fit when you need to search for relationship patterns that are not already stored together in your database. A significant performance penalty is incurred for every additional table that needs to be joined for a query. That is why SQL databases are notoriously bad at deducting relationships from datasets. Graph databases however are really good at this task. In this talk we discuss potential application areas of graph databases in existing open source CMSs like Drupal.
CLASS:PUBLIC STATUS:CONFIRMED CATEGORIES:Graph URL:https:/fosdem.org/2017/schedule/2017/schedule/event/graph_drupal_cms_neo4j/ LOCATION:H.2214 ATTENDEE;ROLE=REQ-PARTICIPANT;CUTYPE=INDIVIDUAL;CN="Kristof Van Tomme":invalid:nomail END:VEVENT BEGIN:VEVENT METHOD:PUBLISH UID:5332@FOSDEM17@fosdem.org TZID:Europe-Brussels DTSTART:20170204T115000 DTEND:20170204T122000 SUMMARY:Incremental Graph Queries with openCypher DESCRIPTION:How can we evaluate a global query on huge graphs in 0.1 seconds? Given our current technology, that would be magic. The lack of wizarding skills did not stop us, however, from tackling the problem by using smart caching structures, which are witchcrafts on their own.
Why is this challenge important? Several applications evaluate global queries on continuously changing graphs: fraud detection in financial transactions, analysis of source code repositories and validating engineering models. Current approaches employ domain-specific optimizations, which are difficult and error-prone to implement. Meanwhile, the requirements of these (and similar) use cases could be uniformly addressed by incremental graph query evaluation. With this technique, the first execution of the queries takes some time, but once the result are calculated, they can be efficiently maintained for each change in the graph.
To allow incremental queries on property graphs, we implemented the ingraph engine, based on the openCypher language specification. We aim to support the standard subset of openCypher, as most standard constructs can be calculated incrementally. We already mapped some of the standard constructs to relational algebra, defined incremental relational algebraic operators and implemented them in an incremental relational engine using Akka actors.
We start the talk by presenting use cases that evaluate complex global queries on continuously changing graphs and discuss the idea of incremental graph queries. We show the mapping of basic openCypher constructs (e.g. MATCH
, WHERE
, WITH
, RETURN
) to relational operators, such as joins, selections and projections. Finally, we show our approach for optimizing incremental graph queries and outline related challenges.
Target audience: Developers, looking for a deeper understanding of openCypher and/or facing complex queries on continuously changing graphs
CLASS:PUBLIC STATUS:CONFIRMED CATEGORIES:Graph URL:https:/fosdem.org/2017/schedule/2017/schedule/event/graph_incremental_queries_open_cypher/ LOCATION:H.2214 ATTENDEE;ROLE=REQ-PARTICIPANT;CUTYPE=INDIVIDUAL;CN="Gabor Szarnyas":invalid:nomail END:VEVENT BEGIN:VEVENT METHOD:PUBLISH UID:5237@FOSDEM17@fosdem.org TZID:Europe-Brussels DTSTART:20170204T123000 DTEND:20170204T130000 SUMMARY:Twitter Streaming Graph with Gephi DESCRIPTION:Presentation of the original vision of the Twitter Stream Importer, from start to current situation with "experience feedback". If possible showing a demo real-time and "open discussion" about improvement on going, visualisation usage etc...
CLASS:PUBLIC STATUS:CONFIRMED CATEGORIES:Graph URL:https:/fosdem.org/2017/schedule/2017/schedule/event/graph_streaming_twitter_gephi/ LOCATION:H.2214 ATTENDEE;ROLE=REQ-PARTICIPANT;CUTYPE=INDIVIDUAL;CN="Matthieu Totet":invalid:nomail END:VEVENT BEGIN:VEVENT METHOD:PUBLISH UID:5071@FOSDEM17@fosdem.org TZID:Europe-Brussels DTSTART:20170204T131000 DTEND:20170204T134000 SUMMARY:Bringing the Semantic Web closer to reality DESCRIPTION:Presentation of an investigation into how Python's RDFLib and SQLAlchemy can be used to leverage PostgreSQL's capabilities to provide a persistent storage back-end for Graphs, and become the elusive practical RDF triple store for the Semantic Web (or simply help you export your data to someone who's expecting RDF)!
CLASS:PUBLIC STATUS:CONFIRMED CATEGORIES:Graph URL:https:/fosdem.org/2017/schedule/2017/schedule/event/graph_postgres_graphdb/ LOCATION:H.2214 ATTENDEE;ROLE=REQ-PARTICIPANT;CUTYPE=INDIVIDUAL;CN="Jimmy Angelakos":invalid:nomail END:VEVENT BEGIN:VEVENT METHOD:PUBLISH UID:5297@FOSDEM17@fosdem.org TZID:Europe-Brussels DTSTART:20170204T135000 DTEND:20170204T142000 SUMMARY:(Cypher)-[:ON]->(ApacheFlink)<-[:USING]-(Gradoop) DESCRIPTION:Graph pattern matching is one of the most interesting and challenging operations in graph analytics. However, it is primarily supported by graph database systems such as Neo4j but, besides research prototypes, not generally available for distributed (not-only graph) processing frameworks like Apache Flink or Apache Spark.
In our talk, we want to give an overview of our current implementation of Cypher on Apache Flink. Cypher is the Neo4j graph query language and enables the intuitive definition of graph patterns including structural and semantic predicates. As the Neo4j graph data model is not supported out-of-the box by Apache Flink, we leverage Gradoop, a Flink-based graph analytics framework based on Apache Flink that already provides an abstraction of schema-free property graphs.
We will give a brief overview about the technologies used to implement Cypher, explain our query engine and give a demonstration of the available language features. Finally, we will discuss open challenges and missing features hopefully motivating people to contribute.
The project is a cooperation between the University of Leipzig and Neo Technology.
CLASS:PUBLIC STATUS:CONFIRMED CATEGORIES:Graph URL:https:/fosdem.org/2017/schedule/2017/schedule/event/graph_cypher_on_flink/ 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:5302@FOSDEM17@fosdem.org TZID:Europe-Brussels DTSTART:20170204T143000 DTEND:20170204T150000 SUMMARY:From Shopping Baskets to Structural Patterns DESCRIPTION:Mining frequent itemsets is an established approach to data mining and supported by productive data mining solutions. For example, one can get insights about buyers’ behavior by analyzing frequent co-occurrences of products in shopping baskets. In contrast, frequent subgraph mining (FSM), the graphy variant of frequent itemset mining, not only evaluates entity co-occurrence but also relationships among entities, i.e., structural patterns. However, existing implementations are all research prototypes which are tailored to textbook problems.
In our talk, we want to give an introduction to the FSM problem on distributed collections of graphs and our implementation in Gradoop, an open source system for scalable graph analytics based on Apache Flink. In contrast to other iterative graph algorithms like page rank, in FSM the search space is dropped but intermediate results of iterations are the desired result. Here, the major technical challenge is the respective usage of Flinks’ distributed iterations.
We will explain different implementation approaches, discuss implementation details which influence scalability and show benchmark results.
CLASS:PUBLIC STATUS:CONFIRMED CATEGORIES:Graph URL:https:/fosdem.org/2017/schedule/2017/schedule/event/graph_structural_patterns/ LOCATION:H.2214 ATTENDEE;ROLE=REQ-PARTICIPANT;CUTYPE=INDIVIDUAL;CN="André Petermann":invalid:nomail END:VEVENT BEGIN:VEVENT METHOD:PUBLISH UID:5244@FOSDEM17@fosdem.org TZID:Europe-Brussels DTSTART:20170204T151000 DTEND:20170204T154000 SUMMARY:Designing a graph library for JavaScript DESCRIPTION:While a lot of languages already have comprehensive libraries to handle graph data (like networkx in python), the same cannot be said for JavaScript.
This talk discusses how we were driven to create a multipurpose graph object specification for JavaScript and what were the design issues we soon had to tackle from implementation choices to API naming.
The result of this work is the graphology specification & reference implementation which we present to open it to feedbacks & contributions.
We will first review the state of the art of graph libraries for JavaScript and discuss the issues which brought us to create a new projet from scratch.
Then we will explain why we chose to design an open specification for a standard API rather than only providing a library.
Finally we will present the reference implementation & the attached library of algorithms and justify the technical choices made, before presenting our future roadmap and related projects (a new version of the graph rendering library, sigma.js, notably).
Alexis Jacomy
Alexis is CTO of Matlo, a data discovery web app. He spent last years working on tools to help people explore data. He also develops multiple JavaScript tools with Guillaume Plique, notably sigma.js to visualize networks in web pages.
https://github.com/jacomyal
Guillaume Plique
Guillaume Plique is a developer at SciencesPo's médialab working on a variety of Open Source projects in JavaScript, Clojure & Python. He specializes in developing software for social sciences researchers and often works with graphs, natural language processing and UIs.
https://github.com/Yomguithereal
CLASS:PUBLIC STATUS:CONFIRMED CATEGORIES:Graph URL:https:/fosdem.org/2017/schedule/2017/schedule/event/graph_library_javascript/ LOCATION:H.2214 ATTENDEE;ROLE=REQ-PARTICIPANT;CUTYPE=INDIVIDUAL;CN="Guillaume Plique":invalid:nomail END:VEVENT BEGIN:VEVENT METHOD:PUBLISH UID:5633@FOSDEM17@fosdem.org TZID:Europe-Brussels DTSTART:20170204T155000 DTEND:20170204T162000 SUMMARY:Graph Processing on SAP HANA, express edition DESCRIPTION:With the recent announcement of the SAP HANA, express edition, a free to use and streamlined version of SAP HANA that can run on laptops and other resource-constrained hosts, it is now possible to use the SAP HANA Graph functionality for free in own graph projects and applications with up to 32GB main memory limit. In addition, the SAP HANA, express edition offers development utilities that can be used to develop own applications on top of SAP HANA.
In this presentation, we will provide an overview of SAP HANA Graph, a native graph processing engine, which is tightly integrated into the database kernel of SAP HANA. It allows to freely combine graph, relational, text, and geospatial processing on a single transactional data representation without the need to replicate data into specialized systems. In particular, we will use the SAP HANA, express edition running on the speaker's laptop to demonstrate the audience a walk-through of the steps to get started with SAP HANA Graph. Specifically, we will show how to execute openCypher queries in SAP HANA and also showcase GraphScript, a high-level domain-specific graph query language for imperative and complex graph query processing.
CLASS:PUBLIC STATUS:CONFIRMED CATEGORIES:Graph URL:https:/fosdem.org/2017/schedule/2017/schedule/event/graph_processing_sap_hana/ LOCATION:H.2214 ATTENDEE;ROLE=REQ-PARTICIPANT;CUTYPE=INDIVIDUAL;CN="Thomas Fischer":invalid:nomail END:VEVENT BEGIN:VEVENT METHOD:PUBLISH UID:5540@FOSDEM17@fosdem.org TZID:Europe-Brussels DTSTART:20170204T163000 DTEND:20170204T170000 SUMMARY:Graph Analytics on Massively Parallel Processing Databases DESCRIPTION:As graph processing moves to the mainstream, a large number of specialized graph engines have emerged. However, for many enterprises, much of their important data resides in relational databases and SQL is the most common workload. So is it reasonable to suggest that relational data processing engines can be used to solve graph problems in a productive and performant manner?
The answer to this question is: “Yes!”
In this talk, we will address the use of massively parallel processing (MPP) databases for graph analytics workloads. We will share some recent findings from the Apache MADlib (incubating) project, including design of graph data structures, implementation of common graph algorithms, and performance results.
CLASS:PUBLIC STATUS:CONFIRMED CATEGORIES:Graph URL:https:/fosdem.org/2017/schedule/2017/schedule/event/graph_analytics_massively_parallel_processing_databases/ LOCATION:H.2214 ATTENDEE;ROLE=REQ-PARTICIPANT;CUTYPE=INDIVIDUAL;CN="Frank McQuillan":invalid:nomail END:VEVENT BEGIN:VEVENT METHOD:PUBLISH UID:5536@FOSDEM17@fosdem.org TZID:Europe-Brussels DTSTART:20170204T171000 DTEND:20170204T174000 SUMMARY:Graphs at scale DESCRIPTION:In the era of docker, big data and micro services it is really important to distribute your applications reasonable across your cluster and keep a good overview of all of your applications. This fact is well addressed, but it would be great to utilize your cluster in that way, that you locate your data in the same cluster like the rest of your application landscape and share your physical resources all over your running applications, no matter if you are serving user requests, crunching data or calculating big graphs. For this reason we brought the awesome graph database Neo4j to run natively on top of Apache Mesos and DC/OS.In this session we will see how easy it is to install, run, operate and scale out Neo4j Causal Clusters on top of Apache Mesos and DC/OS and which possibilities you have spanning big graphs across your cluster.
CLASS:PUBLIC STATUS:CONFIRMED CATEGORIES:Graph URL:https:/fosdem.org/2017/schedule/2017/schedule/event/graph_scale_mesos_dcos_neo4j/ LOCATION:H.2214 ATTENDEE;ROLE=REQ-PARTICIPANT;CUTYPE=INDIVIDUAL;CN="Jörg Schad":invalid:nomail END:VEVENT BEGIN:VEVENT METHOD:PUBLISH UID:5396@FOSDEM17@fosdem.org TZID:Europe-Brussels DTSTART:20170204T175000 DTEND:20170204T181500 SUMMARY:Network Traffic Analysis of Hadoop Clusters DESCRIPTION:Cybersecurity is a broad topic and many commercial products are related to it. We demonstrate a fundamental concept in network analysis: re-construction and visualization of temporal networks. Furthermore, we apply the method to describe operational conditions of a Hadoop cluster. Our experiments provide first results and allow a classification of the cluster state related to current workloads. The temporal networks show significant differences for different operation modes. In reallity we would expect mixed workloads. If such workload parameters are known, we are able to handle a-typical events accordingly - which means, we are able to create alerts based on context information, rather than only the package content.We show an end-to-end example: (1) Data collection is done via python, using the sniffer script; (2) using Apache Hive and Apache Spark we analyze the network traffic data and create the temporary network. Finally, we are able to visualize the results using Gephi in step (3). In a next step, we plan to contribute to the Apache Spot project.
CLASS:PUBLIC STATUS:CONFIRMED CATEGORIES:Graph URL:https:/fosdem.org/2017/schedule/2017/schedule/event/graph_traffic_analysis_hadoop_patterns/ LOCATION:H.2214 ATTENDEE;ROLE=REQ-PARTICIPANT;CUTYPE=INDIVIDUAL;CN="Mirko Kämpf":invalid:nomail END:VEVENT END:VCALENDAR