Data Warehousing und Data Mining


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1 Data Warehousing und Data Mining Multidimensionale Indexstrukturen Ulf Leser Wissensmanagement in der Bioinformatik
2 Content of this Lecture Multidimensional Indexing GridFiles Kdtrees Ulf Leser: Data Warehousing und Data Mining 2
3 Multidimensional Queries Conditions on more than one attribute Combined through AND (intersection) or OR (union) Partial queries: Conditions on some but not all dimensions Selects subcubes 2D: All beverage sales in March D: All beverage sales in 2000 in Berlin to male customers Ulf Leser: Data Warehousing und Data Mining 3
4 Composite Indexes month_id product_id Point X Y P1 2 2 P2 2 2 P3 5 7 P4 5 6 P5 8 6 P6 8 9 P7 9 3 Imagine composite index on (X, Y) Box queries: efficiently supported Partial queries All points/rectangles with X coordinate between Efficiently supported All points/rectangles with Y coordinate between Not efficiently supported Ulf Leser: Data Warehousing und Data Mining 4
5 Composite Index One index with two attributes (X, Y) Prefix of attribute list in index must be present in query The longer the prefix in the query, the better Alternatives Also index (Y, X) 5 attributes: O(5!) orders Combinatorial explosion, infeasible for more than 2 attributes Use independent indexes on each attribute Ulf Leser: Data Warehousing und Data Mining 5
6 Independent Indexes One index per attribute Index on X Index on Y Partial match query on one attribute: supported Partial match query on many attributes Compute TID lists for each attribute Intersect Ulf Leser: Data Warehousing und Data Mining 6
7 Independent versus Composite Index Consider 3 dimensions of range 1,..., points, uniformly (and randomly) distributed Index blocks hold 50 keys or records Index on each attribute has height 4 Find points with 40 x 50, 40 y 50, 40 z 50 Using independent indexes Using xindex, we generate TID list X ~ Using yindex, we generate TID list Y ~ Using zindex, we generate TID list Z ~ For each index, we have /50=2004 IO TIDs are sorted in sequential blocks, each holding 50 TIDs Hopefully, we can keep the three lists in main memory Intersection yields ~1.000 points with 6012 IO Ulf Leser: Data Warehousing und Data Mining 7
8 Independent versus Composite Index Consider 3 dimensions of range 1,..., points, uniformly (and randomly) distributed Index blocks hold 50 keys or records Index on each attribute has height 4 Find points with 40 x 50, 40 y 50, 40 z 50 Using composite index (X,Y,Z) Number of indexed points doesn t change Key length increases assume blocks hold only 30 (10) keys or records Index has height 5 (6) This is worst case index blocks only 50% filled Total: 5 (6) +1000/30 (10) ~ 38 IO (104) Ulf Leser: Data Warehousing und Data Mining 8
9 Conclusion 1 We want composite indexes Much less IO Things get worse for bigger d TID lists don t fit into main memory paging, more IO Reading large TIDs lists again and again is more work than scanning relation once Linear scanning of relation might be faster Advantage of composite indexes grows exponentially with number of dimensions and selectivity of predicates Things get complicated if data is not uniformly distributed Dependent attributes (age weight, income, height, ) Clustering of points Histograms Ulf Leser: Data Warehousing und Data Mining 9
10 Conclusion 2 But: For partial queries, we would need to index all combinations Solution: Use multidimensional indexes Generally an improvement, but many problems prevail Curse of dimensionality remains Most md indexes degrade for many dimensions All md indexes have some worstcase data distribution Usually far from normal or equal distribution Bad space usage, excessive management cost, Alternative: Bitmap indexes Need to load values for all tuples and all dimensions But: Very small memory footprint, fast BIT operations Ulf Leser: Data Warehousing und Data Mining 10
11 Multidimensional Indexes All dimensions are equally important Neighbors are (hopefully) stored on nearby blocks Locality property Difficult to achieve, very important Supported queries Exact match point queries Partial match point queries, box queries (range queries) Nearest neighbor queries Difference to B*trees All Btrees need a total order on the keys For more than one dimension, no such order exists Ulf Leser: Data Warehousing und Data Mining 11
12 Content of this Lecture Multidimensional Indexing GridFiles General Structure Splits Kdtrees Ulf Leser: Data Warehousing und Data Mining 12
13 GridFile??? Referenz Classical multidimensional index structure Simple: searching, indexing, deleting Good for uniformly distributed data Does not handle skewed data very well Many variations Design goals Index structure for point objects Support exact, partial match, range, and neighborhood queries Guaranteed two IO access Under some assumptions All dimensions are treated the same Adapts dynamically to the number of points Ulf Leser: Data Warehousing und Data Mining 13
14 Fixed GridFile [Does not adapt to data distribution at all] Idea Split space into equalspaced cuboids or cells We need maximal and minimal values in all dimensions Directory stores one pointer for each cell Points to block storing pointers to records Directory Index blocks Records / table Ulf Leser: Data Warehousing und Data Mining 14
15 Operations Problem: one index block can hold only m pointers Deleting a point Compute cell using coordinates Search cell in directory and load index block Search point and delete, if present Index block may become empty Eventually, the entire structure may consist of many empty blocks (unused space) Ulf Leser: Data Warehousing und Data Mining 15
16 Operations Inserting a point Locate and load index block If free space: insert point If no free space: generate overflow block No adaptation to skewed data distributions May degenerate to linear search if all points fall in the same (set of) block Ulf Leser: Data Warehousing und Data Mining 16
17 Principle of GridFiles Partition each dimension into disjoint intervals (scales) The intersection of all intervals defines all grid cells Convex ddimensional cuboids Grid directory holds pointer to index blocks per cell When cell overflows split cell (no overflow blocks) Each point falls into exactly one grid cell Many grid cells (a region) may point to the same index block Ulf Leser: Data Warehousing und Data Mining 17
18 Exact Point Search Finding a block for a given point Keep scales for each dimension in memory Lookup point coordinates in scales and derive grid coordinates Map grid coordinates to index block on disk Complexity We assume that the directory is in main memory Other techniques exist, i.e., B*tree over grid coordinates Load index block (1st IO) Search point in index block Access record following pointer (2nd IO) Guaranteed 2 IO Ulf Leser: Data Warehousing und Data Mining 18
19 Range Query, Partial Match Query Range query Compute grid cell coordinate for each end point All grid directory entries in that range may contain qualifying points Partial match query Compute partial grid cell coordinates All grid directory entries with these coordinates may contain points Ulf Leser: Data Warehousing und Data Mining 19
20 Content of this Lecture Multidimensional Indexing GridFiles General Structure Splits Kdtrees Ulf Leser: Data Warehousing und Data Mining 20
21 Inserting Points If index block has free space no problem Otherwise (1st split): Split cuboid Choose a dimension and a scale to split Create new scale, create new index block, distribute points according to the chosen split Insert point This implicitly splits all other grid cells with the same scale Choice of dimension and scale to split is difficult Optimally, we would like to split as many very full index blocks as evenly as possible This is an optimization problem in itself Ulf Leser: Data Warehousing und Data Mining 21
22 Example Imagine one index block holds 3 pointers maximum Note: Usually we have unevenly spaced intervals New point causes overflow Where should we split? Vertical split Splits 2 (3,4)point blocks Leaves one 3point block Horizontal split Splits 2 (3,4)point blocks Leaves one 3point block Need to consider O(d i n1 ) regions Ulf Leser: Data Warehousing und Data Mining 22
23 Inserting Points 2 Otherwise (none 1stsplit) Check borders of index block wrt to scales Borders form a minimal cuboid? Proceed as if it was a 1sttime split Otherwise Split region of index block into smaller cells Consider only not yet realized scales as potential split Create new index block, redistribute points Ulf Leser: Data Warehousing und Data Mining 23
24 GridFile Example 1 (from J. Gehrke) (N=6) A 1 6 A 2 5 A Ulf Leser: Data Warehousing und Data Mining 24
25 GridFile Example 2 (N=6) A B A B A B Ulf Leser: Data Warehousing und Data Mining 25
26 GridFile Example 3 (N=6) A 14 B A B C B C A B C Ulf Leser: Data Warehousing und Data Mining 26
27 GridFile Example 4 (N=6) A D 14 B 1 6 A D B B 13 7 C B C B C A B C D Ulf Leser: Data Warehousing und Data Mining 27
28 GridFile Example 5 (N=6) y 4 y 3 A H I D F B A A H I D D F F B B y 2 y 1 E C G A I G F B E E G F B C C C C B x 1 x 2 x 3 x 4 Ulf Leser: Data Warehousing und Data Mining 28
29 Deleting Points Search point and delete If regions become almost empty, merge index blocks A merge is the removal of a split Must build larger convex regions This can become very difficult Potentially, more than two regions need to be merged to keep convexity condition Example: Where can we merge regions?? A A H I D D F F B B A I G F B E E G F B C C C C B Ulf Leser: Data Warehousing und Data Mining 29
30 Conclusions GridFiles always split parallel to the dimension axes This is not always optimal Use others than rectangles as cells: circles, polygons, etc. Might not disjointly fill the space any more Allow overlaps  R trees Good: Good bucket fill degrees Bad: Grid directory grows very fast Good: Two IO guarantee (if directory fits into memory) Each split finally becomes valid for the entire grid Need not be realized immediately, but restricts later choices Bad adaptation to unevenly skewed data The more dimensions, the worse Ulf Leser: Data Warehousing und Data Mining 30
31 Content of this Lecture Multidimensional Indexing GridFiles Kdtrees Ulf Leser: Data Warehousing und Data Mining 31
32 kdtree??? REF GridFile disadvantages All hyperregions of the ndimensional space are eventually split at the same dimension / scale First cell that overflows determines split This choice is global and never undone kdtrees Multidimensional variation of binary search trees Hierarchical splitting of space into regions Regions in different subtrees may use different split positions Better adaptation to clustering of data than GridFiles kdtree is a main memory data structure kdbtrees: IOoptimization for layout of inner nodes (not here) Ulf Leser: Data Warehousing und Data Mining 32
33 General Idea Binary, rooted tree Paths are selected by dimension / value x<3 x 3 Dimensions are not statically assigned to levels of the tree Data points are only stored in leaves A leaf stores all points in a ndim hypercube with m border planes (m n) y<1 (2,0) y (0,4) (1,1) y<3 (3,1) 1 x<5 (4,6) (3,3) y 3 y< y < 2 7 x 5 (5,6) y 2 (6,4) Ulf Leser: Data Warehousing und Data Mining 33
34 Example the Brick wall x<3 x 3 y<1 (2,0) y (0,4) (1,1) 1 x<5 y< 7 x 5 (4,9) 10 y<3 (3,1) y 3 (4,6) (3,3) y < 2 y 2 (6,4) 10 Ulf Leser: Data Warehousing und Data Mining 34
35 Local Adaptation Ulf Leser: Data Warehousing und Data Mining 35
36 Search Operations Exact point search? Partial match query? Range query? Nearest Neighborhood? Ulf Leser: Data Warehousing und Data Mining 36
37 Search Operations Exact point search In each inner node, decide direction based on split condition Search leaf for searched point Partial match query If dimension of condition in inner node is part of the query proceed as for exact match Otherwise, follow all children in parallel (multiple search paths) Range Query Ulf Leser: Data Warehousing und Data Mining 37
38 kdtree Insertion Search data block of leaf If free space available insert, done Otherwise, chose split dimension and position for this block This is a local decision; remains stable for the future of the subtree Find dimension and split that divides set of points into two sets Consider current points and split in sets of approximately equal size Consider known distributions of values in different dimensions Use alternation scheme for dimensions Finding optimal split points is expensive for high dimensional data (point set needs to be sorted in each dimension) use heuristics Wrong decisions in early splits lead to tree degradation CS students at HU: Don t split at sex, place of birth, But we don t know which points will be inserted in future Ulf Leser: Data Warehousing und Data Mining 38
39 Summary We only scratched the surface Other: Partitioned hashing, Rtree, QuadTeee, X tree, hb tree, R+ tree, UB tree, Store objects more than once; other than rectangular shapes; map coordinates into integers; Curse of dimensionality Your intuition plays tricks on you The more dimensions, the more difficult Balancing the tree, finding MBBs, split decisions, etc. All structures begin to degenerate somehow Often, linear scanning of objects is quicker Or: Compute lowerdimensional, relationshippreserving approximations of objects and filter on those Ulf Leser: Data Warehousing und Data Mining 39
40 Literatur [GG98] Gaede, V. and Günther, O. (1998). "Multidimensional Access Methods." ACM Computing Surveys 30(2): ??? Gehrke??? KD Tree??? GridFiles Ulf Leser: Data Warehousing und Data Mining 40
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