SLIPO. Scalable Linking and Integration of Big POI data. Giorgos Giannopoulos IMIS/Athena RC

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1 SLIPO Scalable Linking and Integration of Big POI data I n f o r m a ti o n a n d N e t w o r ki n g D a y s o n H o ri z o n B i g Da ta Public-Priva te Partnership To p i c : I C T 14 B i g D a t a P P P : C ro s s - s e c tori a l a n d cross - li ngual da ta i ntegra tion and experimenta tion Giorgos Giannopoulos IMIS/Athena RC 1

2 SLIPO is an Horizon 2020 project o Full title: SLIPO - Scalable Linking and Integration of Big POI data o Objective: ICT : Big Data PPP: cross-sectorial and crosslingual data integration and experimentation o GA: o Duration: 36m (1/ /2019) 2

3 Consortium Athena RC is an RTD organization with expertise in Big Data, Linked Data, and large-scale Geospatial applications (Coordinator) InfAI is a Research Institute focused on Linked Data, Semantic Web, and Big Data IAIS is an RTD organization with expertise in data mining, ML, Semantic Web, information retrieval and software engineering TomTom produces, curates, markets, and delivers POIs to hundreds of millions of users and companies worldwide WiGeoGIS is among Europe s leading companies in spatial business intelligence and geomarketing GET is a high-tech SME, with solutions covering the entire lifecycle of geospatial data 3

4 What are POIs? o Points of Interest o Our lives evolve around locations (everyday decisions, complex planning) o POIs are the cornerstone of any application even remotely related to our physical surroundings (navigation, social media, tourism, logistics,...) o The holy trinity of maps: roads, addresses, POIs o What is there? How do I get there? Where do I find something? o POIs are actually complex entities o Geospatial features (not just points, polygons) o Attributes and metadata (e.g., name, type, functionality) o Relations to each other (e.g., containment, part of) 4

5 The significance of POIs for our Economy o Multi-billion, cross-domain and cross-border industry o Almost all Data Economy leaders are producing, using, and offering POIs in their products and services o Incorporates most domains of our economy (mobility, tourism, logistics, manufacturing) o POIs are contained in a location but do not have borders! o Improvements in the value of POIs propagate throughout our Economy o Direct and indirect: productivity gains, optimization of value chains, matchmaking consumers with goods, new value added services o POIs are one of the foundations and value multipliers of our Digital Economy 5

6 POI production o Rapidly changed in the last years with even more data sources o Open, crowdsourced, social o Greater volume, heterogeneity, diversity, veracity, and timeliness o Structured, semi-structured, unstructured o POI enrichment, curation, update even more collaborative o Stakeholders and users involved in all steps of the value chain o Crowdsourcing is a valid and valuable business practice o Harnessed by established POI providers (e.g., TomTom) and giving rise to new companies (e.g., Foursquare, Yelp) focused on the domainspecific collection and provision of POIs 6

7 The POI value chain (1/2) o The goal: o Provide POIs with great quality and value (coverage, accuracy, richness, timeliness) o Reduce effort, update cycles, time-to-market o Data of the value chain o Field surveys (POIs collected in the field) o Proprietary catalogues (e.g., Yellow pages, official registers) o Open data (e.g., schools, plazas, government buildings) o Crowdsourced (e.g., OpenStreetMap) o User contributed (e.g., correct POI metadata, add new POI) o Auxiliary (e.g., check-ins, traffic) 7

8 The POI value chain (2/2) o Who is exploiting it and for what business reasons o Geospatial data producers (mapping products), Mobility sector (routing, LBS), Web companies (search, advertising, interactive maps), Social Media (advertising) o But not only: expansive value chain o POI products and services used across domains and borders o Consumers are also producers o Contribute POIs and corrections o Update POIs with domain-specific metadata 8

9 Data assets o TomTom POI datasets M POIs (~120M facts) o Austrian POIs for businesses - 400K POIs (~1M facts) o OpenStreetMap - 3Billion points, 250M ways, 1.5K categories, 2.5M users o Geonames - 11M place names, 645 categories o Wikimapia - 25M places (~250M facts), 2.5M users, 1.5K Categories o Dbpedia - 700K places o o o o o Floating Car Data - 15 Trillion measurements INSPIRE/Geostat - 3M records Frequency of cars and pedestrians (DE, CH) - 180Billion measurements GDELT -0.25Billion records Yahoo Flickr- 99.2M photos 9

10 POI integration o POI data are by nature ambiguous with no common identifiers o Even searching for POIs is based on pseudo-identifiers (addresses, coordinates, place-names) o Remains labor-intensive o Lack of standardization in data models o Formats and identifiers o Inherent ambiguity o Long update cycles o Fragmented and out of context POIs 10

11 Everyone is challenged o Google Places API offers ~100M POIs worldwide o Coverage is actually low o Cross-examining with official statistics (e.g., hotel registries), actual coverage is 20-40% o Spatial granularity is lacking o Point information only (not polygons), accuracy 5-50m o Timeliness and completeness is hit or miss o Working hours, renamed restaurants 11

12 Linked Geospatial Data o Linked data address the scalability challenges of integrating, enriching, and querying semantically diverse Big Data assets o In GeoKnow, Linked Data were: o effectively applied to maximize value extracted from proprietary, open, and crowdsourced geospatial Big Data o validated in tourism and logistics as a cost-effective and scalable solution for the quality-assured integration, enrichment and sharing of generic-purpose geospatial data 12

13 POI integration with LD technologies o Linked data technologies can address the limitations, gaps, and challenges of integrating, enriching, and sharing Big POI Data in a scalable and quality assured manner o How? Transfer POI integration to the Linked Data domain o Apply the research output of GeoKnow to the specific challenge of POI data o Extend and adapt parts of the Linked Data stack for POI data o Base linking of POIs with OpenStreetMap o Abstract LD complexity and allow the industry to focus on their task: increase the value of their data 13

14 The SLIPO approach o Model POIs in RDF o Flexible, enhanced, semantically rich, encapsulate diverse attributes, evolution, quality o Transform POIs to/from RDF o Support most de facto POI formats, transfer data integration in the LD domain, export integrated POI data sets from LD o The virtuous circle of data integration (interlink, enrich, fuse, QA) o Expand coverage, completeness, richness (lower quality) o Increase quality (reduce coverage) o Repeat! 14

15 The POI lifecycle in SLIPO 15

16 The SLIPO toolkit 16

17 Results o SLIPO Toolkit o Software for quality-assured POI data integration, including transformation, interlinking, fusion, enrichment and analytics o SLIPO APIs o Lightweight and highly reusable APIs exposing the full functionality of the SLIPO Toolkit, following the SaaS paradigm o POI Ontology and mappings o An open, extensible ontology modelling geospatial, temporal, and thematic information of POIs, as well as mappings for de facto POI data formats o Open POI data o A massive, open Linked POI dataset based on OSM 17

18 Evaluation and Exploitation (1/2) o Joint activity that also serves as the first direct exploitation of SLIPO o KPI-based evaluation inside each organization o Actual data assets, expert users, and business goals o Three (3) real-world applications (TomTom, WiGeoGIS, GET) o Personal navigation devices and real-time services (focus: coverage, size, update cycles) o Location intelligence products and custom geo-intelligence studies (focus: lower costs, avoid costly proprietary data, link with marketing metadata) o SaaS mapping products for tourism, culture, and real-estate (focus: coverage, richness, focus on open/crowdsourced sources) 18

19 Evaluation and Exploitation (2/2) o Establish baseline data integration methods and KPIs o POI data value: coverage, richness, accuracy, timeliness o Business value: effort & cost savings o Compare SLIPO vs. baseline in terms of POI data and business value o Individual assessment by each partner o Monitor roll-out (domain, scope, scalability, usability) o Different weighting in criteria depending on priorities o Direct integration of SLIPO (data, software) following validation results o Relatively streamlined (i.e. update POI data sets) o Piece-wise (e.g., only for certain areas, categories) o No Linked Data knowledge required (black-box) 19

20 Thank you 20