1. Big Data Quality Challenges for the Internet of Things (IoT) Vassilis Christophides INRIA Paris (MUSE team)

2. The Internet of Things (IoT) 1  Networks of physical objects (aka. things) with embedded sensing and actuating capabilities that communicate with other objects and information systems item identification (tagging things) sensors (feeling things) nanotechnology (shrinking things) … “Things” are highly heterogeneous: Small (RFID tag) or Big (car) Fixed (fridge) or Mobile (activity tracker) Environment (thermostat) or Person- oriented (body analyzer)

3. Urban Computing 2

4. 3 physical exercise sleep quality biochemical markers psychological state genetics Quantified Self

5. Big Data = Transactions+Interactions+Observations Increasing Data Variety, Velocity, & Veracity Megabytes Gigabytes Terabytes Petabytes IoT devices are reporting even more personal data than humans are! hortonworks.com/blog/7-key-drivers-for-the-big-data-market

6. Adapted from www- 05.ibm.com/fr/events/netezzaDM_2012/Solutions_Big_Data.pdf 5 The 5+1 Vs of Big Data Variability* Data in Change Evolving data distributions, models etc. Insights+ Understanding Automation+ Optimization Value

7. IoT Data Value Chain 6 http://www.codit.eu/how-can-we-help/internet-of-things Capture, track & monitor Transmit data to external env. Ingest, store & integrate data Analyze data, control & automate PROCESS ANALYSE

8.  Agility: – Availability: almost real-time, any- place – Accessibility: mostly in verticals, privacy constrained  Relevance: – Fitness: depends on the granularity of observations & measurements in thematic, spatial & temporal dimensions  Usability: – Trustworthiness: observations or measurements in the wild 7 IoT Data Quality (DQ)

9.  Reliability: – Accuracy: depends on device calibration & sensing method – Validity: depends on the resources constraints (connectivity, bandwidth, power, memory, storage & processing capabilities) of devices and data infrastructure – Completeness: due to data variety a complete domain knowledge is infeasible due to data variability domain knowledge quickly becomes obsolete – Integrity: usually relative to a collection of raw data series originating from different devices 8 IoT Data Quality (DQ) Ben Stansall/Agence France-Presse/Getty Images

10. In Search of IoT DQ Solutions  Let the data speak for itself! – Learn models (semantics) from the data robust to the presence noise (and anomalies) – Detect deviations of data from learned models – Evolve learned models according to data deviations  Computing with Big Data! – Volume: Scalable algorithms (efficiency vs accuracy) – Variety: Looking at condition and context of data deviations – Velocity: Incremental and online algorithms 9 Data Quality: the “other” Face of Big Data B. Saha, D. Srivastava ICDE 2013

11. Towards DQ-aware IoT Analytics 10  Analyze a single data stream: – How we can incrementally detect deviations from data regions of normal behavior? – How we can distinguish between data glitches, meaningful events or even malicious attacks? – What types of data deviations can be identified (distance, density, contextual) and at what granularity level?  Analyze multiple data streams: – How we can compute online correlations across time/space in case missing or delayed data ? – How we can progressively evolve extracted knowledge patterns (motifs, episodes)?

12. 11 http://fr.slideshare.net/wclquang/the-analytics-value- chain-key-to-delivering-business-value-in-iot Key Analytics to Delivering Value in IoT

13. Thank you! 12

14. The Three Domains of Information 13 Source: Barry Devlin, “The Big Data Zoo --- Taming the Beasts

15. Computing with Things: Challenges  Things are different than servers in a Data Center: they are used in the wild, and they are often constrained by limited connectivity, bandwidth, power, memory, storage & processing capabilities  Things are different from UI clients: they don’t usually dispose on-board an UI inheriting more by a M2M communication than UI client-to-server interaction paradigm  Things may directly communication with peers: It isn't all thin-client communication to the parent server in the cloud and hub-and-spoke model presents serious limitations for very large number of devices 14

16. 15 re-workblog.tumblr.com

17. 16 http://www.kdnuggets.com/2015/08/patterns-streaming-realtime-analytics.html