1. Sheila K. Hoffman Ph.D. Candidate in Museology, Heritage and Cultural Mediation, UQÀM Ph.D. Candidate in Art History, Université de Paris I, Panthéon-Sorbonne The Language of Museums, 2015 Annual Conference sheila.hoffman@gmail.com From Bits to Bytes to Buffet: Big Data and Museum Collections

2. What we will talk about: Big Data The term The theory Collections data (Break out) Takeaways The Language of Museums, 2015 Annual Conference sheila.hoffman@gmail.com

3. Here’s what we won’t talk about: Gallery Gadgetry Marketing Data “Users”

4. What is Big Data? Does not compute. Big Data ≠ statistics. | “Big Data is less about data that is big than it is about a capacity to search, aggregate, and cross-reference large data sets.” -Boyd Crawford.

5. What’s the Big Deal about Big Data? 1.Observation 2.Description, explanation, & theorization 3.Simulation and modeling 4.Big Data – New patterns and dimensions of knowledge

6. KNOWLEDGE DATA INFORMATION DataInfoKnowledge ≠≠ The Language of Museums, 2015 Annual Conference sheila.hoffman@gmail.com

7. So, how big is “Big”? Periodic Near Real Time Real Time Data Velocity Data Variety Data Base Web Audio Photo Mobile Video Social MB GB TB PB Data Volume Batch Table

8. …and 3 more Vs The Language of Museums, 2015 Annual Conference sheila.hoffman@gmail.com L e v e r a g i n g o f D a t a C e r t a i n t y o f D a t a C o m p r e h e n s i o n o f D a t a V a l u e : V i s u a l i z a t i o n : V e r a c i t y :

9. Creator (Nationality) Dates Title / Description Date Materials Size Inscription Credit R.I.P. Collections Documentation

10. Collections Data “ 1) The value of a collection depends in the highest degree upon the accuracy and fullness of the records of the history of the object which it contains. 2) A museum specimen without a history is practically without value, and had much better be destroyed than preserved.” George Brown Goode, Principles of Museum Administration, 1895 The Language of Museums, 2015 Annual Conference sheila.hoffman@gmail.com

11. Brainstorm If [object] did not exist tomorrow, what traces (data/ documentation) of it would we want to have remaining? The Language of Museums, 2015 Annual Conference sheila.hoffman@gmail.com

12. Heritage Sector Data Collection Tech 1.Artist (Dates) 2.Title 3.Date 4.Size 5.Materials 6.Credit 7.Location 8.Image(s) 1.Visualization and simulation 2.4D models 3.Virtual reconstruction 4.Digital restoration 5.Digital sequencing and time lapse 6.Virtual environments 7.Depth/surface maps 8.Texture mapping 9.Digital terrain mapping 10.Area and volume mapping 11.Digital point cloud mapping 12.Light mapping/prediction 13.Interactive storytelling systems 14.Augmented Reality 15.Game systems 16.Research 17.Search by Image 18.Search by sound 19.Search by audio 20.Search by video 21.Search by geolocation 22.Machine translation 23.Model Comparison 24.Optimized Character Recognition 25.Facial recognition 26.Pattern recognition computer vision 27.Event detection 1.Image 2.Sound 3.Video 4.History (non-linked) 5.Associated objects (Internal) 6.Associated people 7.Associated records 8.Associated dates 9.Associated work by color 10.Keyword search 11.Social Tag 12.Geotag 13.Share 14.Comment 15.Print 16.Use Image filter 17.Create Virtual Gallery 18.Embed or copy 19.See metadata 20.Review 21.Photosynth 22.3D Model 23.Link to other sources 28.Motion estimation 29.Object recognition 30.Image restoration 31.Color comparison 32.Material comparison 33.Audio comparison 34.Name phonetic (text phonetic) comparison 35.Art genome 36.Think maps, visual thesaurus, heuristic chart 37.Haptic feedback 38.Optical Microscopy 39.X-ray radiography 40.Micro-FTIR Spectrometry Fourier transform infrared spectroscopy 41.SEM-EDS Energy-dispersive X-ray spectroscopy 42.GC-MS Gas chromatography– mass spectrometry 43.XRF - X-Ray fluorescence 44.UV-VIS-Spectrometry Ultraviolet–visible spectroscopy 45.LC-DAD-MS Liquid Chromatography with Diode Array Detection and Mass Spectrometry 46.LC-Ion trap MS 47.Laser Raman Spectrometry 48.MALDI-TOF-MS Matrix- assisted laser desorption/ionization Common Fields of Data Collection Occasional Use Used in related cultural sectors The Language of Museums, 2015 Annual Conference sheila.hoffman@gmail.com

13. Cutting through the clutter 1.Open Photo Policy 2.Create a Personal Digital Proficiency Plan 3.Create a Collections Data Expansion Plan 4.Adopt a Technology Plan Computational capacity 1965 1975 1985 1995 2005 2015 Moore’s Law

14. The Language of Museums, 2015 Annual Conference sheila.hoffman@gmail.com The 7 th V of Big Data: Vous (baby steps matter!)

15. From Bits to Bytes to Buffet: Big Data and Museum Collections Thank you! sheila.hoffman@gmail.com @UQAMcuratrix