Summary of the Stream Reasoning workshop at ISWC 2016

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Brief report about the contents of the Stream Reasoning workshop at SIWC 2016. Additional info about the event are available at: http://streamreasoning.org/events/sr2016

Technology

Stream Reasoning 2016
D. Dell'Aglio, E. Della Valle, T. Eiter and
M. Krötzsch
http://streamreasoning.org/events/sr2016
Report on SR2016
Daniele Dell’Aglio
dellaglio@ifi.uzh.ch http://dellaglio.org @dandellaglio

http://streamreasoning.org/events/sr2016
Heterogeneity in RSP engines
 We have several RDF Stream Processing engines
• C-SPARQL, CQELS, SPARQLstream, EP-SPARQL...
 Heterogeneity in
• Languages
• Features
• Query models
 Challenge 1: interoperability and comparison
• R. Keskisärkkä: Query Templates for RDF Stream Processing
• X. Ren, H. Khrouf, Z. Kazi-Aoul, Y. Chabchoub and O. Cure:
On measuring performances of C-SPARQL and CQELS
2

http://streamreasoning.org/events/sr2016
Stream Reasoning techniques
 Can we exploit the semantics in the stream to
improve the processing?
• Window management
• Maintenance
 Challenge 2: Techniques and methods for Stream
Reasoning
• J. Z. Pan (invited speaker): The Maze of Deletion in Stream
Reasoning
• R. Yan, M. T. Greaves, W. P. Smith and D. L. McGuinness:
Remembering the Important Things: Semantic Importance in
Stream Reasoning
3

http://streamreasoning.org/events/sr2016
Stream Reasoning in applications
 SR starts to be mature to be used in real use cases
 However, new challenges are emerging
• Modelling
• Noise
• Algorithms and scalability
 Challenge 3: SR in real settings
• F. Lecue (invited speaker): Ontology Stream Reasoning for
Diagnosis and Predictive Inference
• P. Schneider, J. Parreira and T. Eiter: Towards Spatial
Ontology-Mediated Query Answering over Mobility Streams
• H. Khrouf, B. Belabbess, L. Bihanic, G. Kepeklian and O.
Curé: WAVES: Big Data Platform for Real-time RDF Stream
Processing
4

What's hot

Query Rewriting in RDF Stream Processing

Jean-Paul Calbimonte

Abstract. Many approaches have been proposed for Stream Reasoning (SR). Some of them combine information flow processing (IFP) techniques and semantic technologies to make sense in real-time of noisy, vast and heterogeneous data streams that come from complex domains. More recent works shown the presence of a trade-off between through- put and reasoning expressiveness. Indeed, systems with IFP-like performance are not really expressive (e.g. up to an RDFS subset) and vice versa. For static data, Information Integration (II) systems approached the problem already. The idea consists in spreading the reasoning complexity over different layers of an hierarchical architecture and treating it where it is easier to do. Is it possible realize an expressive and efficient stream reasoning (E2SR), by defining a hierarchical approach that adapts II techniques to the streaming scenario? In this paper, I discuss my plan towards E2SR, the intuition of adapting Information Integration techniques to the streaming scenario and the need of Stream Reasoning of comparative analysis to support its technological progress.

A Hierarchical approach towards Efficient and Expressive Stream Reasoning

Riccardo Tommasini

Benchmarks like LSBench, SRBench, CSRBench and, more recently, CityBench satisfy the growing need of shared datasets, ontologies and queries to evaluate window-based RDF Stream Processing (RSP) engines. However, no clear winner emerges out of the evaluation. In this paper, we claim that the RSP community needs to adopt a Systematic Comparative Research Approach (SCRA) if it wants to move a step forward. To this end, we propose a framework that enables SCRA for window based RSP engines. The contributions of this paper are: (i) the requirements to satisfy for tools that aim at enabling SCRA; (ii) the architecture of a facility to design and execute experiment guaranteeing repeatability, reproducibility and comparability; (iii) Heaven – a proof of concept implementation of such architecture that we released as open source –; (iv) two RSP engine implementations, also open source, that we propose as baselines for the comparative research (i.e., they can serve as terms of comparison in future works). We prove Heaven effectiveness using the baselines by: (i) showing that top-down hypothesis verification is not straight forward even in controlled conditions and (ii) providing examples of bottom-up comparative analysis.

Heaven: A Framework for Systematic Comparative Research Approach for RSP Engines

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RSP-QL*: Querying Data-Level Annotations in RDF Streams

keski

Presentation of short paper submitted to OrdRing workshop, held at ISWC 2014 - http://streamreasoning.org/events/ordring2014. In the last years, there has been an increase in the amount of real-time data generated. Sensors attached to things are transforming how we interact with our environment. Extracting meaningful information from these streams of data is essential for some application areas and requires processing systems that scale to varying conditions in data sources, complex queries, and system failures. This paper describes ongoing research on the development of a scalable RDF streaming engine.

Towards efficient processing of RDF data streams

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RDF-Gen: Generating RDF from streaming and archival data

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Programming with rich data frequently implies that one needs to search for, understand, integrate and program with new data - with each of these steps constituting a major obstacle to successful data use. In this talk we will explain and demonstrate how our approach, LITEQ - Language Integrated Types, Extensions and Queries for RDF Graphs, which is realized as part of the F# / Visual Studio- environment, supports the software developer. Using the extended IDE the developer may now a. explore new, previously unseen data sources, which are either natively in RDF or mapped into RDF; b. use the exploration of schemata and data in order to construct types and objects in the F# environment; c. automatically map between data and programming language objects in order to make them persistent in the data source; d. have extended typing functionality added to the F# environment and resulting from the exploration of the data source and its mapping into F#. Core to this approach is the novel node path query language, NPQL, that allows for interactive, intuitive exploration of data schemata and data proper as well as for the mapping and definition of types, object collections and individual objects. Beyond the existing type provider mechanism for F# our approach also allows for property-based navigation and runtime querying for data objects.

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SPARQL-Generate is an extension of SPARQL 1.1 for querying not only RDF datasets but also documents in arbitrary formats. The solution bindings can then be used to output RDF (SPARQL-Generate) or text (SPARQL-Template) Anyone familiar with SPARQL can easily learn SPARQL-Generate; Learning SPARQL-Generate helps you learning SPARQL. The open-source implementation (Apache 2 license) is based on Apache Jena and can be used to execute transformations from a combination of RDF and any kind of documents in XML, JSON, CSV, HTML, GeoJSON, CBOR, streams of messages using WebSocket or MQTT... (easily extensible) Recent extensions and improvement include: - heavy refactoring to support parallelization - more expressive iterators and functions - simple generation of RDF lists - support of aggregates - generation of HDT (thanks Ana for the use case) - partial implementation of STTL for the generation of Text (https://ns.inria.fr/sparql-template/) - partial implementation of LDScript (http://ns.inria.fr/sparql-extension/) - integration of all these types of rules to decouple or compose queries, e.g.: - call a SPARQL-Generate query in the SPARQL FROM clause - plug a SPARQL-Generate or a SPARQL-Template query to the output of a SPARQL- Select function - a Sublime Text package for local development

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Graph relationships are everywhere. In fact, more often than not, analyzing relationships between points in your datasets lets you extract more business value from your data. Consider social graphs, or relationships of customers to each other and products they purchase, as two of the most common examples. Now, if you think you have a scalability issue just analyzing points in your datasets, imagine what would happen if you wanted to start analyzing the arbitrary relationships between those data points: the amount of potential processing will increase dramatically, and the kind of algorithms you would typically want to run would change as well. If your Hadoop batch-oriented approach with MapReduce works reasonably well, for scalable graph processing you have to embrace an in-memory, explorative, and iterative approach. One of the best ways to tame this complexity is known as the Bulk synchronous parallel approach. Its two most widely used implementations are available as Hadoop ecosystem projects: Apache Giraph (used at Facebook), and Apache GraphX (as part of a Spark project). In this talk we will focus on practical advice on how to get up and running with Apache Giraph and GraphX; start analyzing simple datasets with built-in algorithms; and finally how to implement your own graph processing applications using the APIs provided by the projects. We will finally compare and contrast the two, and try to lay out some principles of when to use one vs. the other.

Introduction into scalable graph analysis with Apache Giraph and Spark GraphX

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Within this tutorial we present the results of recent research about the cloud enablement of data streaming systems. We illustrate, based on both industrial as well as academic prototypes, new emerging uses cases and research trends. Specifically, we focus on novel approaches for (1) fault tolerance and (2) scalability in large scale distributed streaming systems. In general, new fault tolerance mechanisms strive to be more robust and at the same time introduce less overhead. Novel load balancing approaches focus on elastic scaling over hundreds of instances based on the data and query workload. Finally, we present open challenges for the next generation of cloud-based data stream processing engines.

Cloud-based Data Stream Processing

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First impressions of SparkR: our own machine learning algorithm

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Using H2O Random Grid Search for Hyper-parameters Optimization

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The aim of the EU FP 7 Large-Scale Integrating Project LarKC is to develop the Large Knowledge Collider (LarKC, for short, pronounced “lark”), a platform for massive distributed incomplete reasoning that will remove the scalability barriers of currently existing reasoning systems for the Semantic Web. The LarKC platform is available at larkc.sourceforge.net. This talk, is part of a tutorial for early users of the LarKC platform, and describes the data model used within LarKC.

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Summary of the Stream Reasoning workshop at ISWC 2016

1. Stream Reasoning 2016 D. Dell'Aglio, E. Della Valle, T. Eiter and M. Krötzsch http://streamreasoning.org/events/sr2016 Report on SR2016 Daniele Dell’Aglio dellaglio@ifi.uzh.ch http://dellaglio.org @dandellaglio

2. http://streamreasoning.org/events/sr2016 Heterogeneity in RSP engines  We have several RDF Stream Processing engines • C-SPARQL, CQELS, SPARQLstream, EP-SPARQL...  Heterogeneity in • Languages • Features • Query models  Challenge 1: interoperability and comparison • R. Keskisärkkä: Query Templates for RDF Stream Processing • X. Ren, H. Khrouf, Z. Kazi-Aoul, Y. Chabchoub and O. Cure: On measuring performances of C-SPARQL and CQELS 2

3. http://streamreasoning.org/events/sr2016 Stream Reasoning techniques  Can we exploit the semantics in the stream to improve the processing? • Window management • Maintenance  Challenge 2: Techniques and methods for Stream Reasoning • J. Z. Pan (invited speaker): The Maze of Deletion in Stream Reasoning • R. Yan, M. T. Greaves, W. P. Smith and D. L. McGuinness: Remembering the Important Things: Semantic Importance in Stream Reasoning 3

4. http://streamreasoning.org/events/sr2016 Stream Reasoning in applications  SR starts to be mature to be used in real use cases  However, new challenges are emerging • Modelling • Noise • Algorithms and scalability  Challenge 3: SR in real settings • F. Lecue (invited speaker): Ontology Stream Reasoning for Diagnosis and Predictive Inference • P. Schneider, J. Parreira and T. Eiter: Towards Spatial Ontology-Mediated Query Answering over Mobility Streams • H. Khrouf, B. Belabbess, L. Bihanic, G. Kepeklian and O. Curé: WAVES: Big Data Platform for Real-time RDF Stream Processing 4

Summary of the Stream Reasoning workshop at ISWC 2016

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