Searching for Meaning in the Era of Big Data and IoT

Transcription

1 Searching for Meaning in the Era of Big Data and IoT Trung Tran MIT Lincoln Labs GraphEx Conference 11 May 2016 Distribution Statement A

2 MTO Strategy EM Spectrum Tactical Information Extraction Globalization Operating efficiently in a congested environment while unlocking previously unused portions of the spectrum Enable resourceconstrained systems to coordinate, collect and analyze data at the tactical edge Create a new business model to engage the global electronics market for the DoD

3 The Era of Big (Wasted) Data Useful Data Tagged Analyzed Big gap in information analytics! Edge Analytics is critical. [M. Horowitz, et al., CPU DB; IDC, 2012] 3

4 Other Trends Driving Edge Analytics X 700M = 121K EFLOPs Computing resources at the edge outstrip resources in the cloud and data centers PFLOPS 173 GFLOPs Source: Wikipedia f Network economics & reliability are affecting the cost of moving raw data from the edge to center of the network. The links simply may not be available Responsiveness demands local processing when immediate actions are required Source: Defense-update.com Data ownership, accessibility, security limit where data can be processed and stored 4

5 Other Trends Driving Edge Analytics Device autonomy requires that sensors must be Able to respond to changes, evaluate their data, and automatically re-task themselves Source: theinquirer.net Dynamic peer-to-peer device interaction must be done in a closed loop machine to machine network f Users demand more control over what data is collected and what information that data provides to third parties Redundancy is critical when downtime is not an option edge analytics can identify faults and re-route information to users Source: forum.xcitefun.net 5

6 Moving from Intelligence to Reasoning What Could Be True? Inductive reasoning Imagination Creativity Deductive reasoning Rational Proven What Is True? Abductive Reasoning What is most likely True? Theories Decisions Observations 6

7 Moving from Intelligence to Reasoning Concrete Deductive reasoning What we observe Inductive reasoning Undefined Unclear Our interpretation Understood 7

8 Difference Knowledge and Information Identification Classification Fusion Cognitive Layer Information Layer Communications Layer Source: nwlink.com

9 Changing Our Approach to Analytics Batch Dense Offline Centralized Supervised learning Trends analysis Streaming Sparse Real time Distributed Active/reinforced learning Reasoning Advances Bayesian inference nets Streaming graph analytics Tensor math Reinforced learning Impact Creates associations for abstract ideas Enables ML of bayes nets HW acceleration (multi-dimensional graphs) Local customization 9

10 Data Analytics: Classic Data Mining DATA MINING Modeling KNOWLEDGE Information understood and explained Visualization, Validation Discovery Modeling Model Knowledge is evaluated by a user Information is compared against expected results Data Processing INFORMATION Data, organized and placed in context Data Mining Search Operations Data Transform Object Base Information is searched based on context Information is formatted and sorted DATA Observations and Measurements Data Cleansing Sensor Sensor Sensor High Speed Networks Data is Filtered Data is Collected adapted from Integrated Data Mining and Fusion Concept Edward Waltz

11 Analytics Today: Processing in the Cloud DATA MINING: Centralized and Manual Modeling KNOWLEDGE Information understood and explained Visualization, Validation Discovery Modeling Model Time 10% Data Processing INFORMATION Data, organized and placed in context HDFS Data Mining Search Operations Data Transform Object Base 5% 80% DATA Observations and Measurements Data Cleansing Sensor Sensor Sensor 5% 11

12 Streaming Analytics: Processing at the Edge DATA MINING: Distributed and Automated Adaptive Modeling KNOWLEDGE Information understood and explained Visualization, Validation Discovery Modeling Model Information is compared against expected results Stream Processing INFORMATION Data, organized and placed in context Data Mining Search Operations Data Transform Object Base Information is searched based on context Information is formatted and sorted DATA Observations and measurements Data Cleansing Sensor Sensor Sensor Data is filtered from intelligent sensors Identification Classification Data is Collected 12

13 Introducing Guided Exploration Systems Reinforced Learning Source: John Hopkins Continuous exploratory learning (Wave 3 of ML) Reinforcement learning Based on supervised training Guided by humans 13

14 Labeling the Data - Applying Graph Analytics Causal models (Bayes, Markov, etc..) Associate events with outcomes Can be predictive Can be complex/hard to manage Graph Analytics Embeds associations Nodes = Events Edges= Associations Sub-graphs Possible outcomes Complexity (Features = Events) associated with (Classifications: Outcomes) 14

15 Using Streaming Graph Analytics: Predicting Outcomes Real-Time Streaming Graph built on Current Observations Bayesian Net With Labeled Outcomes Outcome A Offline Bayesian Net Representing All Possible Outcomes Outcome B Outcome C = Outcome B

16 User-Guided Reinforced Learning Could be A or B; definitely not C Definitely B Prioritized information list Available sensor feeds A priori knowledge New data requirements Changed events Changed associations Key piece of information 16

17 10 Today s Processing Architectures don t Support Streaming 1. Store sensor data onto network Network copy (Peta Bytes) Hard Drive latency ~ 8,000 cycles per random data access 2. Retrieve data from network 6. Write data back to network Memory Transfer 5. Read/write data into RAM during processing Processor Idle Working Copy (Gigabytes) Processor Active 3. Make local copy For a typical system today: 950 Watts of 1 KWatt = movement of data 50 Watts = actual computation. (source Intel Shekhar Borkar) 4. Read data into cache Write data after processing Local copy (Terabytes) DRAM latency ~ 200 cycles per random data access

18 Matrix Operations Today: Problem with Memory CPU Core L1 I$ L1 D$ MemIO (source Intel Shekhar Borkar) 18

19 Better Memory Management is Critical MemIO CPU Core L1 I$ L1 D$ Arbiter CPU Core L1 I$ L1 D$ Efficient scheduling of data transfers Minimize cache hits Exploit locality of data Improve memory bus throughput Efficient load balancing of CPU cores Delta not absolute addressing Allows for data compression Handles short rows Allows for sub block formation Either done in HW or SW SW examples Stanford Deep Dive SW HW example MIT LL s FPGA 19

20 Going Beyond Memory Management CPU Core L1 I$ LiM Multiplication Core LiM Merge Core LiM Merge Core L1 D$ edram Scratchpad Arbiter MemIO 20