From Bits to Bytes to Buffet Big Data

From Bits to Bytes to Buffet: Big Data and Museum Collections 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

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

Here’s what we won’t talk about: • Gallery Gadgetry • Marketing Data • “Users” The Language of Museums, 2015 Annual Conference sheila. hoffman@gmail. com

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 The Language of Museums, 2015 Annual Conference sheila. hoffman@gmail. com

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 The Language of Museums, 2015 Annual Conference sheila. hoffman@gmail. com

Data ≠ Info ≠ Knowledge N O I MAT IN FOR DAT A KNOWLEDGE The Language of Museums, 2015 Annual Conference sheila. hoffman@gmail. com

So, how big is “Big”? Data Velocity e m Ti e m i al Re T l a e R r a Ne d o i r Pe ic h tc a B MB GB TB PB Data Volume Tabl e Data Ph Bas e ot o So Web ci udio A al Data Variety Vid eo Mobile The Language of Museums, 2015 Annual Conference sheila. hoffman@gmail. com

…and 3 more Vs Val a t a D f o ue: g n i g a r e v e L a t a D f o y t n i a t r e C : y t i c a Ver Com pre : n h ens o i t i on a z i o f D l a u a ta s i V The Language of Museums, 2015 Annual Conference sheila. hoffman@gmail. com

R. I. P. Collections Documentation Creator (Nationality) Dates Title / Description Date Materials Size Inscription Credit The Language of Museums, 2015 Annual Conference sheila. hoffman@gmail. com

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

Brainstorm I f [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

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

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 1965 1975 1985 The Language of Museums, 2015 Annual Conference 1995 2005 Computational capacity Moore’s Law 2015 sheila. hoffman@gmail. com

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

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