2. Vertical Data In Data Processing, you run up against two curses immediately. Curse of cardinality: solutions don’t scale well with respect to record volume. "files are too deep!" Curse of dimensionality: solutions don’t scale with respect to attribute dimension. "files are too wide!" The curse of cardinality is a problem in both the horizontal and vertical data worlds! In the horizontal data world it was disguised as “curse of slow joins”. In the horizontal world we decompose relations to get good design (e.g., 3 rd normal form), but then we pay for that by requiring many slow joins to get the answers we need.

3. Horizontal Processing of Vertical Data or HPVD, instead of the ubiquitous Vertical Processing of Horizontal (record orientated) Data or VPHD. Parallelizing the processing engine.  Parallelize the software engine on clusters of computers.  Parallelize the greyware engine on clusters of people (i.e., enable visualization and use the web...). Again, we need better techniques for data analysis, querying and mining because of: Parkinson’s Law: Data volume expands to fill available data storage. Moore’s law: Available storage doubles every 9 months! Techniques to address these curses.

4. Yield prediction: Using Remotely Sensed Imagery (RSI) consists of an aerial photograph (RGB TIFF image taken ~July) and a synchronized crop yield map taken at harvest; thus, 4 feature attributes (B,G,R,Y) and ~100,000 pixels. A stronger association, “hi_NIR & low_red  hi_yield”, found through HPVD data mining), allows producers to take and query mid-season aerial photographs for low_NIR & high_red grid cells, and where low yeild is anticipated, apply (top dress) additional nitrogen. Can producers use Landsat images of China of predict wheat prices before planting? A few HPVD successes: 1. Precision Agriculture TIFF image Yield Map 2. Infestation Detection (e.g., Grasshopper Infestation Prediction - again involving RSI) Grasshopper caused significant economic loss each year. Early infestation prediction is key to damage control. Pixel classification on remotely sensed imagery holds much promise to achieve early detection. Pixel classification (signaturing) has many, many applications: pest detection, Flood monitoring, fire detection, wetlands monitoring … Producer are able to analyze the color intensity patterns from aerial and satellite photos taken in mid season to predict yield (find associations between electromagnetic reflection and yeild). E.g., ”hi_green & low_red  hi_yield”. That is very intuitive.

5. 3. Sensor Network Data HPVD  Micro and Nano scale sensor blocks are being developed for sensing  Biological agents  Chemical agents  Motion detection  coatings deterioration  RF-tagging of inventory (RFID tags for Supply Chain Mgmt)  Structural materials fatigue There will be trillions ++ of individual sensors creating mountains of data which can be data mined using HPVD (maybe it shouldn't be called a success yet?).

6. 4. A Sensor Network Application: Each energized nano-sensor transmits a ping (location is triangulated from the ping). These locations are then translated to 3-dimensional coordinates at the display. The corresponding voxel on the display lights up. This is the expendable, one-time, cheap sensor version. A more sophisticated CEASR device could sense and transmit the intensity levels, lighting up the display voxel with the same intensity. Wherever threshold level is sensed (chem, bio, thermal...) a ping is registered in a compressed structure (P-tree – detailed definition coming up) for that location. Situation space Nano-sensors dropped into the Situation space Soldier sees replica of sensed situation prior to entering space.:.:.:.:..:: ….:. : … : … ::..:.. :: :.: … : :..:..::..::..:.::...:.:.:.:..:: ….:. : … : … ::..:.. :: :.: … : :..:..::..::..:.::...:.:.:.:..:: ….:. : … : … ::..:.. :: :.: … : :..:..::..::..:.::.. Using Alien Technology’s Fluidic Self-assembly (FSA) technology, clear plexiglass laminates are joined into a cube, with a embedded nano-LED at each voxel. ================================== \ CARRIER / CubE for Active Situation Replication (CEASR) The single compressed structure (P-tree) containing all the information is transmitted to the cube, where the pattern is reconstructed (uncompress, display).

7. 3. Anthropology Application Digital Archive Network for Anthropology (DANA) (analyze, query and mine arthropological artifacts (shape, color, discovery location,…)

8. What has spawned these successes? (i.e., What is Data Mining?) Querying is asking specific questions for specific answers Data Mining is finding the patterns that exist in data ( going into MOUNTAINS of raw data for the information gems hidden in that mountain of data.) Raw data must be cleaned of: missing items, outliers, noise, errors Data Warehouse: cleaned, integrated, read-only, periodic, historical database Data Mining Pattern Evaluation and Assay Classification Clustering Rule Mining Task-relevant Data Selection Feature extraction, tuple selection visualization Loop backs Smart files

9. Data Mining versus Querying Even on the Query end, much work is yet to be done (D. DeWitt, ACM SIGMOD Record’02). On the Data Mining end, the surface has barely been scratched. But even those scratches have had a great impact. For example, one of the early scatchers became the biggest corporation in the world. A Non-scratcher had to file for bankruptcy protection. SQL SELECT FROM WHERE Complex queries (nested, EXISTS..) Standard querying FUZZY query, Search engines, BLAST searches OLAP (rollup, drilldown, slice/dice.. Searching and Aggregating Machine LearningData Mining Supervised Learning – classification regression Unsupervised Learning - clustering Walmart vs. KMart There is a whole spectrum of techniques to get information from data : Association Rule Mining Data Prospecting Fractals, … HPVD Approach: Vertical, horizontally horizontal data vertically) HPVD Approach: Vertical, compressed data structures, Predicate-trees or Peano- trees (Ptrees in either case) 1 processed horizontally (Most DBMSs process horizontal data vertically) Ptrees are data-mining-ready, compressed data structures, which attempt to address the curses of cardinality and curse of dimensionality. 1 Ptree Technology is patented by North Dakota State University

10. 0 0 0 0 1 P 11 4. Left half of rt half ? false  0 0 2. Left half pure1? false  0 0 0 1. Whole is pure1? false  0 5. Rt half of right half? true  1 0 0 1 R 11 0 1 To find the number of occurences of 7 0 1 4, AND these basic Ptrees (next slide) Predicate trees (Ptrees): vertically project each attribute, Given a table structured into horizontal records. (which are traditionally processed vertically - VPHD ) Top-down construction of the 1-dimensional Ptree of R 11, denoted, P 11 : Record the truth of the universal predicate pure 1 in a tree recursively on halves (1/2 1 subsets), until purity is achieved. 3. Right half pure1? false  0 0 0 1 0 1 1 1 1 1 0 0 0 1 0 1 1 1 1 1 1 1 0 0 0 0 0 1 0 1 1 0 1 0 1 0 0 1 0 1 0 1 1 1 1 0 1 1 1 1 0 1 1 0 1 0 0 0 1 1 0 0 0 1 0 0 1 0 0 0 1 1 0 1 1 1 1 0 0 0 0 0 1 1 0 0 R 11 R 12 R 13 R 21 R 22 R 23 R 31 R 32 R 33 R 41 R 42 R 43 R[A 1 ] R[A 2 ] R[A 3 ] R[A 4 ] 010 111 110 001 011 111 110 000 010 110 101 001 010 111 101 111 011 010 001 100 010 010 001 101 111 000 001 100 But it is pure (pure0) so this branch ends then vertically project each bit position of each attribute, then compress each bit slice into a basic 1D Ptree. e.g., compression of R 11 into P 11 goes as follows: P 11 pure1? false=0 pure1? true=1 pure1? false=0 R (A 1 A 2 A 3 A 4 ) 2 7 6 1 6 7 6 0 3 7 5 1 2 7 5 7 3 2 1 4 2 2 1 5 7 0 1 4 for Horizontally structured records Scan vertically 010 111 110 001 011 111 110 000 010 110 101 001 010 111 101 111 011 010 001 100 010 010 001 101 111 000 001 100 = Base 10Base 2 P 11 P 12 P 13 P 21 P 22 P 23 P 31 P 32 P 33 P 41 P 42 P 43 0 0 0 0 1 1 0 0 0 0 0 0 1 01 10 0 1 0 0 1 0 0 0 0 1 0 01 0 1 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 10 01 ^^ ^ ^ ^ ^^ 1-Dimensional Ptrees VPHD to find the number of occurences of 7 0 1 4 =2 HPVD to find the number of occurences of 7 0 1 4?

11. 0 1 0 1 1 1 1 1 0 0 0 1 0 1 1 1 1 1 1 1 0 0 0 0 0 1 0 1 1 0 1 0 1 0 0 1 0 1 0 1 1 1 1 0 1 1 1 1 1 0 1 0 1 0 0 0 1 1 0 0 0 1 0 0 1 0 0 0 1 1 0 1 1 1 1 0 0 0 0 0 1 1 0 0 R 11 R 12 R 13 R 21 R 22 R 23 R 31 R 32 R 33 R 41 R 42 R 43 R[A 1 ] R[A 2 ] R[A 3 ] R[A 4 ] 010 111 110 001 011 111 110 000 010 110 101 001 010 111 101 111 101 010 001 100 010 010 001 101 111 000 001 100 To count occurrences of 7,0,1,4 use 111000001100 : 0 P 11 ^P 12 ^P 13 ^P’ 21 ^P’ 22 ^P’ 23 ^P’ 31 ^P’ 32 ^P 33 ^P 41 ^P’ 42 ^P’ 43 = 0 0 01 ^ 7 0 1 4 P 11 P 12 P 13 P 21 P 22 P 23 P 31 P 32 P 33 P 41 P 42 P 43 0 0 0 0 1 10 0 1 0 0 1 0 0 0 0 0 0 1 01 10 0 1 0 0 1 0 0 0 0 1 0 01 0 1 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 10 01 ^ ^^^ ^ ^ ^ ^^ R (A 1 A 2 A 3 A 4 ) 2 7 6 1 3 7 6 0 2 7 5 1 2 7 5 7 5 2 1 4 2 2 1 5 7 0 1 4 010 111 110 001 011 111 110 000 010 110 101 001 010 111 101 111 101 010 001 100 010 010 001 101 111 000 001 100 = This 0 makes entire left branch 0 These 0 s make this node 0 These 1 s and these 0 s (which when complemented are 1's) make this node 1 The 2 1 -level has the only 1-bit so 1-count = 1*2 1 = 2 # change

12. R 11 0 1 0 1 Top-down construction of basic P-trees is best for understanding, bottom-up is much faster (once across). Bottom-up construction of 1-Dim, P 11, is done using in-order tree traversal, collapsing of pure siblings as we go: 0 1 0 1 1 1 1 1 0 0 0 1 0 1 1 1 1 1 1 1 0 0 0 0 0 1 0 1 1 0 1 0 1 0 0 1 0 1 0 1 1 1 1 0 1 1 1 1 1 0 1 0 1 0 0 0 1 1 0 0 0 1 0 0 1 0 0 0 1 1 0 1 1 1 1 0 0 0 0 0 1 1 0 0 R 11 R 12 R 13 R 21 R 22 R 23 R 31 R 32 R 33 R 41 R 42 R 43 P 11 0 0 0 0 0 0 1 0 0 0 0 1 1 1 0 Siblings are pure0 so callapse!

13. Thank you.