11/26/07 – IRADSN’07 1 Stream Hierarchy Data Mining for Sensor Data Margaret H. Dunham SMU Dallas, Texas Vijay Kumar UMKC Kansas City, Missouri

From Sensors to Streams – An Outline nData Stream Overview nData Stream Visualization n Temporal Heat Map nData Stream Modeling n Extensible Markov Model nData Stream Hierarchy 11/26/07 – IRADSN’07 2

From Sensors to Streams – An Outline nData Stream Overview nData Stream Visualization n Temporal Heat Map nData Stream Modeling n Extensible Markov Model nData Stream Hierarchy 11/26/07 – IRADSN’07 3

From Sensors to Streams nData captured and sent by a set of sensors is usually referred to as “stream data”. nReal-time sequence of encoded signals which contain desired information. It is continuous, ordered (implicitly by arrival time or explicitly by timestamp or by geographic coordinates) sequence of items nStream data is infinite - the data keeps coming. 11/26/07 – IRADSN’07 4

Data Stream Management Systems (DSMS) nSoftware to facilitate querying and managing stream data. nRetrieve the most recent information from the stream nData aggregation facilitates merging together multiple streams nModeling stream data to “summarize” stream nVisualization needed to observe in real-time the spatial and temporal patterns and trends hidden in the data. 11/26/07 – IRADSN’07 5

DSMS Problems nStream Management development in state similar to that of databases prior to 1970’s n Each system/researcher looks at specific application or system n No standards concerning functionality n No standard query language nUnreasonable to expect end users will access raw data, data in the DSMS, or even data at a summarized view nDomain experts need to “see” a higher level of data 11/26/07 – IRADSN’07 6

Our Proposal Four level data abstraction to facilitate the creation of actionable intelligence for domain experts evaluating sensor data. 11/26/07 – IRADSN’07 7

From Sensors to Streams – An Outline nData Stream Overview nData Stream Visualization n Temporal Heat Map nData Stream Modeling n Extensible Markov Model nData Stream Hierarchy 11/26/07 – IRADSN’07 8

Assumptions for Our Research nEnd User: n May not be knowledgeable concerning sensors n Probably a Domain Expert n May not need to see exact sensor values n Concerned with trends and approximate values n Need to see data from MANY sensors at one time n Need to see data continuously in a visualization of the stream 11/26/07 – IRADSN’07 9

Suppose There Were MANY Sensors nTraditional line graphs would be very difficult to read nRequirements for new visualization technique: n High level summary of data n Handle multiple sensors at once n Continuous n Temporal n Spatial 11/26/07 – IRADSN’07 10

Temporal Heat Map nAlso called Temporal Chaos Game Representation (TCGR) nTemporal Heat Map (THM) is a visualization technique for streaming data derived from multiple sensors. n It is a two dimensional structure similar to an infinite table. n Each row of the table is associated with one sensor value. n Each column of the table is associated with a point in time. n Each cell within the THM is a color representation of the sensor value nColors normalized (in our examples) n 0 – While n 0.5 – Blue n Red 11/26/07 – IRADSN’07 11

10/11/07 12 NGDM'07 Cisco – Internal VoIP Traffic Data Time → Values → Complete Stream: CiscoEMM.pngCiscoEMM.png VoIP traffic data was provided by Cisco Systems and represents logged VoIP traffic in their Richardson, Texas facility from Mon Sep 22 12:17: to Mon Nov 17 11:29:

Derwent River (UK) 11/26/07 – IRADSN’07 13 Derwent Temporal Heat Map derwentrotate.png

From Sensors to Streams – An Outline nData Stream Overview nData Stream Visualization n Temporal Heat Map nData Stream Modeling n Extensible Markov Model nData Stream Hierarchy 11/26/07 – IRADSN’07 14

Data Stream Modeling Requirements n Summarization (Synopsis )of data n Use data NOT SAMPLE n Temporal and Spatial n Dynamic n Continuous (infinite stream) n Learn n Forget n Sublinear growth rate - Clustering 11/26/07 – IRADSN’07 15

Extensible Markov Model nExtensible Markov Model (EMM): at any time t, EMM consists of a Markov Chain with designated current node, N n, and algorithms to modify it, where algorithms include: n EMMCluster, which defines a technique for matching between input data at time t + 1 and existing states in the MC at time t. n EMMIncrement algorithm, which updates MC at time t + 1 given the MC at time t and clustering measure result at time t + 1. n EMMDecrement algorithm, which removes nodes from the EMM when needed. n In addition, the EMM has associated Data Mining functions such a Rare Event Detection and Prediction Jie Huang, Yu Meng, and Margaret H. Dunham, “Extensible Markov Model,” Proceedings IEEE ICDM Conference, November 2004, pp /26/07 – IRADSN’07 16

10/11/0717 NGDM'07 EMM Learning 1/3 N1 N2 2/3 N3 1/1 1/3 N1 N2 2/3 1/1 N3 1/1 1/2 1/3 N1 N2 2/3 1/2 N3 1/1 2/3 1/3 N1 N2 N1 2/2 1/1 N1 1

11/26/07 – IRADSN’07 18 N2 N1N3 N5N6 2/2 1/3 1/2 N1N3 N5N6 1/6 1/3 EMM Forgetting

11/26/07 – IRADSN’07 19 EMM Sublinear Growth Rate Minnesota Department of Transportation (MnDot)

From Sensors to Streams – An Outline nData Stream Overview nData Stream Visualization n Temporal Heat Map nData Stream Modeling n Extensible Markov Model nData Stream Hierarchy 11/26/07 – IRADSN’07 20

Traditional DBMS Data Abstraction nThree levels of data abstraction n Physical, n Logical n External nData is normally pulled to the user by a query 11/26/07 – IRADSN’07 21

Proposed DSMS Data Abstraction nAbstraction n Level 0 - Physical Level Raw data from sensors Cannot be stored n Level 1 – DSMS Sensor data is merged, aggregated, and cleansed. DSMS queries may be processed against this data. n Level 2 – Model Summarization (Synopsis )of data n Level 3 – Domain Expert Summary Visualization nData is normally pushed to the user 11/26/07 – IRADSN’07 22

11/26/07 – IRADSN’07 23 LevelsLowest Level Highest Level Abstraction Inter-level Data Migration Memory Hierarchy nExternal Storage Subset/Cache/BufferFetch/Prefetch DBMS Data Hierarchy 3Physical Storage External ViewFetch, Prefetch Data Warehouse nOperational Data Cube/ Multidimensional View Aggregation Stream Hierarchy 4Sensor DataVisualization/TriggersAutomatic Push

11/26/07 – IRADSN’07 24

Stream Hierarchy Summary nExcept for the inter-level functionality requirements, each level functionality is independent of the others and may differ across different implementations. nThe model used must capture time and ordering of data, be able to both learn and forget, and use some variation of clustering. nVisualization at the domain expert level must capture both time and ordering. It addition it should be able to be easily “read” for many sets of sensors. 11/26/07 – IRADSN’07 25