Ontologies and big spatial data

Ontologiesand SpatialBig Data

Ines Messadi

Introduction

Role OfOntology InBig Data

The rise ofspatial bigdata

Ontologiesand spatialbig data

ResearchTrends

Conclusion

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Ontologies and Spatial Big Data

Ines Messadi

Information System Techniques MasterNational Engineering School Of Tunis

20. januar 2017

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Ines Messadi

Introduction




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Conclusion

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Outline

1 IntroductionOverview of Big Data

2 Role Of Ontology In Big Data

3 The rise of spatial big data

4 Ontologies and spatial big data

5 Research Trends

6 Conclusion

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Ines Messadi

Introduction




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Conclusion

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Introduction

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Introduction




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Conclusion

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Introduction

Late Decision⇒ Missing Oppotunities

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Introduction




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Conclusion

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Introduction: Overview of big data

Common Definition

• Big data is a common term for data sets that are so largeand complex that conventional storage, retrieval andanalysis techniques become inadequate.

3Vs

• Velocity: Speed• Speed of creating, analyzing, storing data ⇒ Real Time

processing

• Volume

• Variety

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Introduction




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Conclusion

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Role of Ontology in Big Data

Ontology

• An ontology provides a representation of concepts of thereal word, as agreed by a community of people.

• Serves as semantic reference for users or applications thataccept to align their interpretation of the semantics oftheir data to the interpretation stored in the ontology.

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Introduction




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Conclusion

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Role of Ontology in Big Data

Can Ontologies be useful for big data ?

• Yes

• It can bring order out of chaos

• The combination of ontologies andBig Data could provide new solutionsfor many problems.

• Ontologies are importantcomponents for big data integrationand manipulation.

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Introduction




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Conclusion

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How to combine Ontology and Big Data

• Mapping ontology to database• Mapping classes (concepts) defined in ontology to

database schema.• Mapping classes/instances defined in ontology to data in

DB

• Add metadata on data using vocabulary defined inontology

• Convert database (e.g. RDB) to ontology-based(RDF) database

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Introduction




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Conclusion

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How to use the combinations of ontology and bigdata

• Ontology can provide semantics to add raw data.

• Generalized concepts in ontology can connect data invarious concept levels across domains.

• We can use ontology as given knowledge to analysis bigdata.

• ontology can be used in data understanding andintegration task to describe the semantics of theinformation sources

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Conclusion

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There exist many approaches to populate ontology fromvarious data sources.

• Existing ontology library

• Text mining technique

• Reverse engineering technique

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Conclusion

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Some approches to facilitate data integration :

• Single ontology approach All data source schemas aredirectly mapped to a shared global ontology that providesa uniform interface to the user.

• Multiple ontology approach Each data source isrepresented in its own (local) ontology separately.

• Hybrid ontology approach A combination of the twoprevious approaches. Local ontology is built for each datasource schema, without mapping to other local ontologies,but to a global ontology.

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Conclusion

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The rise of spatial big data

• A proliferation of geospatial data on the Web

• Professionally-produced material being offered for free(e.g., Google or Bing maps).

• The public has also been encouraged to make geospatialcontent, including their geographical location, availableonline (e.g., OpenStreetMap).

• There is now a substantial amount of public sectorinformation becoming available as open geospatial dataThe volume of such geospatial Web content is already bigand constantly growing.

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Conclusion

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The rise of Spatial Big Data

How Big is Big ?

Generates about 5 TB of data per day.

Generates about 25 PB of data per day, significantportion of which is spatiotemporal data (images and videos)

Spatial Big Data Sources

• Mobile phones, social media.

• GPS, Satellite remote sensing, Radar, Sensor Networks.etc.

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Conclusion

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The rise of spatial big data

Spatial data ?

Business data that contains or describes location

Geographic features(roards, rivers, parks, etc.)

Street and postal address( customers, stores,factories.etc)Anything associated with a physical location, described bycoordinates.

The large volumes of spatial data provide valuableresources to both the ordinary users and researchers fortheir various uses, and are accessible for retrieval throughDigital Libraries - Geolibrary, GeoPortals, Spatial DataInfrastructures (SDI) and the Web.

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Conclusion

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Big Data vs Spatial Big Data

Is Big Spatial data different from big data

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Conclusion

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Motivation

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Introduction



Ontologies andspatial bigdata

ResearchTrends

Conclusion

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Ontologies and spatial big data

• Space and time can meet ontologies in two distinct ways :• First, they can be the domains described by the ontology.• Space and time can be the implicit background to an

application domain that relies on geographical data. In thiscase we speak about ontologies of geographical domain.

• To deliver well-defined web resources and make themaccessible by end-users and processes. Ontologies play akey-role to realize this ambitious goal.

• They are suitable means to model information with itsmeaning, thus enhancing chances of successful sharing.

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Conclusion

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On spatial ontology

In spatial ontology creation, there are two different majorapproaches:

First, by analyzing a collection of existing spatial databases andmethodologies, a spatial ontology model is defined based onthose databases

The second approach in spatial ontology creation is to define acomplete spatial ontology model.

Unfortunately, little attention has been put up to now intospatial ontologies.

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Conclusion

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On spatial ontology

Much geographic data are represented by a series of x,ycoordinates ⇒ Ontologies can be very useful in describing, forexample, the extent of a mountain or the relation of sacredsites to nearby rivers.

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Conclusion

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On spatial ontology

• Ontologies are the vital to semantic description referencingof geographic information.

• The Protege spatial ontology provides spatial built-ins thatcan be used to provide a spatial dimension to otherontologies when needed.

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Conclusion

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On spatial ontology

To target more of the big data market, Oracle Spatial hassupported RDF and SPARQL since Oracle 10g, and morerecently, IBM’s DB2 has added this support.

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Conclusion

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On Spatial Ontologies

If an ontology is known and used by everybody for annotatinginformation and searching for information, then all the aboveproblems of search and retrieval are eliminated.

• Challenge of effective Spatial Information Retrieval (SIR) :Lack of detailed semantic referencing showing the spatialrelationship among entities of the geospatial data for easyaccessibility by the user.

Figur: Spatial relationships

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Conclusion

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Main semantic relations of geographic concepts

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Conclusion

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Main semantic properties of geographic concepts

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Conclusion

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Research Trends

• The current trend in ontological developments aims atsupporting richer ontology models that enable sharingmore complex information.

• Needs new algorithms to find spatial-temporal correlationsand predict future events.

• Minimize the time it takes to make the data available foranalysis: Data loading time should be minimal to make thedata available for use

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Conclusion

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Research Trends

Weakness of current research

• Research and development in ontology management hasalmost neglected to consider space and time as essentialcomponents of modern information systems.

• Most of current ontologies do not take into account thespatial and temporal characteristics of information.

Need research about spatial big data as well as studies usingspatial big data.

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Conclusion

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Conclusion

• SBD are important to society and ontology are very usefulto SBD.

• New opportunities are emerging from the platforms,analytics, and science perspectives. SBD research is stilllargely underexplored territory.

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Conclusion

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References

• Daniel Cintra Cugler et Al. Spatial Big Data: Platforms,Analytics, and Science.GeoJournal manuscript,University ofMinnesota, Minneapolis, MN, 2013.

• Michael R. Evans et Al. Enabling Spatial Big Data viaCyberGIS: Challenges and Opportunities,Computer ScienceDepartment, University of Minnesota, Minneapolis, MN, 2013.

• Raju Vatsavai and Budhendra Bhaduri. Geospatial Analytics inthe era of Big Data and Extreme Scale Computing, CSEDivision, Oak Ridge National Laboratory, Oak Ridge, TN 37831.

• John Bateman and Scott Farrar. Spatial Ontology Baseline.OntoSpace Project Report,V2, University of BremenGermany,June 2006.

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