Skimmer: Rapid Scrolling of Relational Query Results. Manish Singh, Arnab Nandi and H.V. Jagadish
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1 Skimmer: Rapid Scrolling of Relational Query Results Manish Singh, Arnab Nandi and H.V. Jagadish
2 Information Overload! Hard for users to specify the query results of interest! Empty or many-answers problem! User s often use a non-optimal strategy! Order or rank result w.r.t. their most preferred attribute(s), and! Choose from the top few results Finding a needle in haystack 2
3 Example: A Realtor s Database! Contains many attributes! Buyers have specific needs, but they cannot accurately express it in terms of precise queries.! Typical buyer s workflow:! Express selection predicates on few attributes, and! Plow through the large result set, ordered by some attribute(s).! Example of Information Overload 3
4 Sample Query Result HID # Bedroom School Dist Crime Rate Price
5 Browsing! User interface studies have shown:! Users use both browsing and querying to search information! Users tend to do exploratory browsing before formulating precise queries! How to support fast browsing over relational data?! Relational data lacks visuals aids, and! Without visual aids browsing speed is restricted to user s reading speed. 5
6 Fast Browsing Using Scrolling Interface! Scrolling is a widely used interface! Constraints in fast scrolling:! Technical constraints: Data distortion! Bodily constraints: Visual perception, memory retention etc.! Solution: Integrate scrolling with automatic zooming! Easy transition between overview and detailed information. 6
7 Scrolling Large Text Documents! Our eyes follows visuals hints, such as! Difference in font size, colors! Section headings, graphics etc.! Known structural outline! Variable speed scrolling [Igarashi, UIST 2000]! Automatic zooming based on scrolling rate 7
8 Relational Data Vs. Text Documents! Alphanumeric values with few visual markers! Lacks structural outline 8
9 Problem Definition! Design a variable-speed scrolling system for relational data! Use user s scrolling behavior to determine how much and what information to display! Skimmer: Allows users to skim through information at different browsing speeds. 9
10 Outline! Motivation! Scrolling Interface! User Interface! Goodness Metric! Algorithms! Experimental Evaluation! Conclusion 10
11 User Interface 11
12 User Interface! Input: Sorted query result R! Output: R requires S pages {P 1, P 2,, P S } for display! Display representatives: {D 1, D 2,, D S }! D i P i and it is computed based on:! User s current scrolling speed! Contents of page P i! User s current browsing history! Benefit: Reduces information overload by showing summarized, non-redundant and diverse information 12
13 Fast Browsing Over Houses 13
14 Goodness Metric: Information Loss! Tuplewise information loss of a non-displayed tuple, t nd from P i where t d is most similar tuple from D i and H(sid) TIL ( tnd sid) = V ( t, nd d! Pagewise information loss score of page P i :, t ) PIL( P i, sid ) = TIL( tp, sid ) tp Pi! Cumulative information loss for result set R and scroll log SL 14 CIL( SL, R) = SL sid = 1 PIL( Pi sid, )
15 Outline! Motivation! Scrolling Interface! Algorithms! Two naïve algorithms! Two K-Medoids based algorithms! Relationship between K-Medoid and PIL Score! Three K-Means based algorithms! Experimental Evaluation! Conclusion and Future Work 15
16 Naïve Sampling! Compute set D i = K i tuples from page P i! K i is determined based on user s current scrolling speed "! Random sampling! Pick K i random tuples from P i! Uniform sampling! Pick K i evenly spaced tuples from P i 16
17 K-Medoid! It s a clustering algorithm that partitions a dataset D, containing N elements, into K partitions! Each partition is represented by an actual sample point! It minimizes the following absolute error criterion: E KMedoids ( P) = K j= 1 p Cj V ( p, oj )! Best known heuristic solutions: PAM, CLARA and CLARANS 17
18 Local K-Medoid (LKMed)! D i = PAM (P i, K i )! PAM Algorithm:! Initialize clusters centers! Repeat until convergence A! Assignment: Assign each point to nearest cluster! Update: Swap based greedy update of cluster centers! CLARA and CLARANS not suitable for small datasets B Current Representative 18
19 Importance of History! Our goal: Show non-redundant, diverse information to the user House A 10,000$ House C 40,000$ House B 40,000$ 19
20 Relationship: PIL Score and K-Medoid! K-Medoid optimizes: E KMedoids ( P) = K j= 1 p C V ( p, j oj ) A A B D! PIL Score of page P B C PIL( P) = where L = L j= 1 p C i l= 1 V ( p, oj) j K l and i-1 l= 1 K l < j i l= 1 K l Historical Representative Current Representative 20
21 Historical K-Medoid (HKMed)! D i = HKMed (P i, K i )! Minimizes the exact PIL score! HKMed Algorithm! Initialize the cluster centers! Repeat until convergence A B D! Assignment: Assign each point to nearest cluster. C! Update: Update unfixed cluster centers based on greedy swap Historical Representative Current Representative 21
22 Performance Issues! Computational constraints: Satisfy user s non-linear scrolling behavior! Next page representative is selected based on:! Past displayed content! User s current scroll rate! Desired computation time: Less than 100 ms! PAM : O(K*(N-K) 2 ) dist computations per iteration 22
23 Approximate K-Medoid! K-Means is an efficient partition based clustering algorithm. It divides a dataset into K partitions.! It is O(K*N) as compared to O(K(N-K) 2 ) in K-Medoid! Each partition is represented by partition centroid.! It minimizes the following square-error criterion: EKMeans( P) = K j= 1 p C j p mi 2! It can only be used for numerical attributes and Euclidean distance function. 23
24 Local K-Means (LKMeans)! Algorithm! KCenters = KMeans (P i, K i )! D i = NN (KCenters, P i )! KMeans Algorithm! Initialize cluster centers! Repeat until convergence! Assignment: Assign each point to nearest cluster.! Update: New cluster centers by computing mean of all assigned points. 24
25 Historical K-Means (HKMeans)! Similar motivation as that of historical K-Medoid.! Algorithm! KCenters = HKMeans (P i, K i )! D i = NN (KCenters, P i )! HKMeans Algorithm! Initialize cluster centers! Repeat until convergence! Assignment: Assign each point to nearest cluster.! Update: New unfixed cluster centers by computing mean of all assigned points. Historical Representative 25
26 Effect of Initialization! HKMeans worse than LKMeans in terms of CIL Score! Unlike HKMed, HKMeans can get caught in local minimum! Bad initial cluster centers! Representatives being determined based on the outliers Historical Representative 26
27 Two-Phase K- Means (TPKMeans)! Phase 1! Choose good initial cluster centers using LKMeans! Phase 2! Select non-redundant representatives using HKMeans! Benefits! Information quality quite close to HKMed! Runs almost N times faster as compared to K-Medoids based algorithms 27
28 Two Phase K-Means (TPKMeans) Local K-Means Historical K-Means 28
29 Outline! Motivation! Scrolling Interface! Algorithms! Experimental Evaluation! Performance! Information Quality! User Study! Conclusion and Future Work 29
30 Experimental Goals! Computational Performance! Page size! Number of dimensions! Sampling rate! Information Quality! User Study 30
31 Performance! HKMed and LKMed need more time! Not suitable for large page size or high sampling rate! HKMed is faster than LKMed! All algorithms satisfy interactive response constraint 31
32 Experimental Goals! Computational Performance! Information Quality! Information Gain: We use Random Sampling as baseline B! Page size! Number of dimensions! Sampling rate! User Study CIL IG( A, B) = CIL B A ( SL, R) ( SL, R) 32
33 Information Quality! HKMed is best followed by TPKMeans and LKMed! HKMeans is almost close to random sampling! Information gain decreases with increasing # dimensions 33
34 Summary Recommendations Two-Phase K-Means Page Size Two Phase K-Means Sampling Rates Historical K-Medoids Two Phase K-Means 34
35 Experimental Goals! Computational Performance! Information Quality! User Study! Users efficiency and quality of response to three tasks 35
36 Interesting Patterns Regression Task User Study Discriminating Features! Almost similar or better quality of response for all three tasks! Users are able to do the tasks times faster! Less stress due to reduced information 36
37 Conclusions! Scrolling-aware browsing: Introduced the idea of selecting representative tuples to enable variable-speed scrolling through relational data! Information loss metric: Quantified loss of information incurred due to browsing representative tuples! Algorithms: Developed and compared five new scrolling based sampling algorithms that minimize information loss! Interaction constraints: Proposed efficiently computable algorithms that satisfy fast scrolling requirement. 37
Skimmer: Rapid Scrolling of Relational Query Results
Skimmer: Rapid Scrolling of Relational Query Results Manish Singh University of Michigan Ann Arbor, USA singhmk@umich.edu Arnab Nandi The Ohio State University Columbus, USA arnab@cse.osu.edu H.V. Jagadish
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