1. Remembrance: The Unbearable Sentience of Being Digital Ragib Hasan *, Radu Sion, and Marianne Winslett University of Illinois at Urbana-Champaign Stony Brook University 4 th Biennial Conference on Innovative Database Research January 4-7, 2009

2. What is the difference between … A file, or a database tuple, is a dumb container of values Data, the robot, can remember, has sentience 4 th Conference on Innovative Data Systems Research (CIDR) 2009 2 Data, the android from Star Trek and … Data, stored in databases or file cabinets

3. Our Data objects suffer from Amnesia Since the early days, our data processing model has assumed data containers (tuples, files, variables) to be oblivious of their past 4 th Conference on Innovative Data Systems Research (CIDR) 2009 3 We assume a data object to know only its present value, and not recall its old values, states, or context information

4. Our current data objects are not sentient Database tuples returned in query results only show their latest values Data processing, evaluation is based on only the current state of variables Data objects do not tell us their historical states, or how they were created, processed, transmitted We cannot pick a system, turn a knob, and time-travel to 5 minutes in the past 4 th Conference on Innovative Data Systems Research (CIDR) 2009 4

5. Exploring remembrance in various forms Databases Time-travel/Transaction time databases Temporal SQL Checkpointing 4 th Conference on Innovative Data Systems Research (CIDR) 2009 5 Scientific computing Provenance Lineage File systems Versioning file systems CVS and other SCMs WORM storage Web WayBack archive gMail Systems & languages Reflective systems, self-managed systems Time traveling virtual machines

6. However … Most of these systems are in essence, versioning systems – Memory / history is not an intrinsic property of data – Association between a data value and its history is kept externally These solutions are also isolated, piecemeal, and glued together by our original single-valued, oblivious data paradigm 4 th Conference on Innovative Data Systems Research (CIDR) 2009 6

7. Remembrant Computing We propose a new data paradigm, where – Data objects retain their memories as an intrinsic property – History, context, temporal events can be recalled – Past (memory) and present (value) are considered as an atomic unit of data 4 th Conference on Innovative Data Systems Research (CIDR) 2009 7 Files recall their past contents x = 5 x = 10 Variables remember their past values and context Hard disk blocks recall past content Queries return tuple objects which remember their past context, value, states

8. Remembrant Computing When data objects are transferred, they retain their old memories Copies retain memory of the original, along with copying context Deletions remove the value from container, but the memories may live on 4 th Conference on Innovative Data Systems Research (CIDR) 2009 8

9. But, whats the point of remembering? Time-aware knowledge Associative memories More expressivity in data processing – Compute based on not only present value, but historic information, derivation, lineage, provenance – Mine useful patterns Taint analysis / information flow checking Recover from transient errors at arbitrary granularities Time-travel seamlessly to any point in an application, system, or website 4 th Conference on Innovative Data Systems Research (CIDR) 2009 9

10. Is this possible, viable, desirable? Physical limitations – Only limited amount of memory possible in primary and secondary data storage – Not all memories can be retained forever Problem of Recursion – Will the system to store memories also have its own memory objects? Performance – Handling large amount of history for every data object can cause performance bottleneck 4 th Conference on Innovative Data Systems Research (CIDR) 2009 10

11. Is this possible, viable, desirable? Security / privacy – How do we control access to old memories? – Would remembering states/values violate privacy? Legal issues – Various regulations limit how long data can be retained – Privacy laws limit contextual information that can be recorded 4 th Conference on Innovative Data Systems Research (CIDR) 2009 11

12. Is this possible, viable, desirable? Scalability: How do we recall, and when do we forget? – Remembering everything can be undesirable Some humans suffer from Hyperthymesia or total recall Too many unimportant details can overwhelm functionality – How to decide what memories to forget is an issue – Management, searching, indexing all need to scale with large number of memories 4 th Conference on Innovative Data Systems Research (CIDR) 2009 12

13. Where we are today … MyLifeBits: – Recording all of Gordon Bells personal interactions requires 18GB/year, or 1.1 TB over a lifetime Provenance: – 16% overhead in recording all information flows for files (PASS [Seltzer et al, Usenix Technical 06]) – 3%-15% overhead in secure, tamper evident provenance for files [Hasan et al, Usenix FAST09] 4 th Conference on Innovative Data Systems Research (CIDR) 2009 13

14. Epilogue Ability to recall the past memories, and contextual information differentiates sentient beings from simpler organisms Augmenting data objects with memory as an intrinsic property will introduce sentience for digital objects 4 th Conference on Innovative Data Systems Research (CIDR) 2009 14