Automatic Categorization of Query Results

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Automatic Categorization of Query Results

• A Paper by
• Kaushik Chakarbati, Surajit Chaudhari, Seung -won
  Hwang
• Presented by
• Arjun Saraswat

2
Flow of the Presentation

• 1.Introduction
• 2.Motivation
• 3.Basics of Model
• 4.Cost Estimation
• 5.Algorithms
• 6.Experimental Evaluation
• 7.Conclusion

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INTRODUCTION
4
Introduction

• This paper basically solves the too many
  answers
• problem.
• This phenomenon of too many answers is often
  referred to
• as Information overload .
• Information overload happens when the user is not
  certain
• what she is looking for, In such situations
  user generally
• fires a broad query in order to avoid
  exclusion of potentially interesting results.
• There are two techniques to handle information
  overload
• Categorization and Ranking, this paper talks
  about the
• categorization Technique.

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MOTIVATION
6
Motivation

• Example A user fires a query on the MSN House
  Home
• Database with following specifications
• Area Seattle/Bellevue Area of Washington, USA
• Price Range 200,000 to 300,000
• The query returns 6045 results, it is hard for
  the user to
• Separate the interesting ones from the
  uninteresting ones, which leads to lot of wastage
  of user time and effort.
• This problem is solved by the Categorization
  techniques
• introduced by this paper, such queries are
  answered by
• hierarchal category structure that are based
  on the
• contents of the answer set.
• The main motive is to reduce the information
  overload

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MotivationFig1.Structured hierarchal
categorization results of the Example Query
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Basics of Model
9
Basics of Model

• R set of tuples or it can be either base
  relation or
• materialized view or result of a query Q.
• Q SPJ (select-project-join) query.
• A hierarchal categorization of R is a recursive
  partitioning of
• the tuples in R based on the data attributes and
  their values,
• this is shown in Fig.1.
• Base Case At the root or level 0 contains all
  the tuples in R,
• this tuple set is partitioned into mutually
  disjoint categories
• using a single attribute.
• Inductive Step At a given node C at level
  (l-1), the partitioning
• of set of tuples tset(C) contained in C in
  ordered mutually
• disjoint subcategories (level l nodes) is done
  using the attribute
• which is same for all nodes at level(l-1).

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Basics of Model

• The partitioning of node C is only done if it
  contains more than certain number of tuples
  and the attribute on which it is done is
• called categorizing attribute of level l and
  sub-categorizing
• Attribute of level (l-1).
• An attribute used once is not used again at later
  levels.
• Category Label The predicate label (C)
  describing node C.
• Example Neighborhood Redmond, Bellevue
  and Price 200k - 225k
• Tuple-set (tset(C)) The set of tuples contained
  in C, either
• occurring directly or indirectly under its
  subcategories.
• Example tset for category with label
  'Neighborhood Seattle, is the set of all homes
  in R that are located in Seattle.

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Basics of Model

• Important points to remember for each level
• Determine the categorizing attribute for that
  level.
• Attribute partitioning is done in such a way as
  to minimize the information overload on the user.
• Exploration Model
• It has two models that capture the two common
  scenarios.
• 1.All Scenario.
• 2.One Scenario.

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Basics of Model

• The model of exploration of the subtree rooted at
  an arbitrary
• node C
• EXPLORE C
• if C is non leaf node
• CHOOSE one of the following
• (1)Examine all tuples in tset(C)//option
  SHOWTUPLES
• (2)for(i1ini)//option SHOWCAT
• Examine Label of this subcategory Ci
• CHOOSE one of the following
• (2.1)EXPLORE Ci
• (2.2)Ignore Ci
• else//C is a leaf node
• Examine all tuples in tset(C)//SHOWTUPLES is only
  option

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Basic of Model

• 2.One Scenario
• EXPLORE C
• if C is non leaf node
• CHOOSE one of the following
• (1)Examine all tuples in tset(C) from the
  beginning
• till first relevant tuple found//option
  SHOWTUPLES
• (2)for(i1ini)//option SHOWCAT
• Examine Label of the ith subcategory Ci
• CHOOSE one of the following
• (2.1)EXPLORE Ci
• (2.2)Ignore Ci
• If (choiceExplore) break//examine till first
  relevant tuple
• else//C is a leaf node
• Examine all tuples in tset(C) from beginning till
  first
• relevant tuple found//SHOWTUPLES is only option

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Cost Estimation
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Cost Estimation

• Cost Model for All Scenario
• CostAll (X,T) information overload cost or
  simply cost.
• X a given user exploration.
• T Tree.
• We want to generate the tree that would minimize
  the number of items this particular user needs to
  examine.
• We use the aggregate knowledge of previous user
  behavior in order to estimate the information
  overload cost CostAll(T) that a user will face,
  on average, during an exploration using a given
  category tree T.

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Cost Estimation

• Exploration Probability The probability P(C)
  that the user exploring T
• explores category C, using either SHOWTUPLES or
  SHOWCAT, upon
• examining its label.
• SHOWTUPLES Probability The probability Pw(C)
  that the user goes for
• option SHOWTUPLES for category C given that she
  explores C. The
• SHOWCAT probability of C.
• Cost Model for All Scenario
• Consider a Non-Leaf Node C of T
• CostAll(Tc) cost of exploring the subtree Tc
  rooted at C we denote
• CostAll(Tc) by CostAll (C) as we know the cost is
  always calculated in
• context of the given Tree.

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Cost Estimation

• If SHOWTUPLES is Chosen for C then Cost
• Pw(C)tset(C)
• If SHOWCAT is Chosen for C the Cost
• Cost of first component K n (where K is the
  cost of examining a category label relative to
  the cost of examining a data tuple.)
• Cost of Second Component CostAll (Ci), if she
  chooses to explore Ci, 0 if she chooses to
  ignore it.
• CostAll (C) Pw(C)tset(C)(1-Pw(C)) (Kn
  S P(Ci)CostAll(Ci)) (1)
• If C is leaf node then CostAll (C) tset(C)

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Cost Estimation

• Cost Model for ONE Scenario
• CostOne(T) information overload cost Let us
  consider the
• Cost for SHOWTUPLES Option Pw(C)frac(C)tset(
  C)
• Cost for SHOWCAT option
• (1-Pw(C)) S (Prob. that Ci is the first
  category explored (Ki CostOne (Ci)))
• Total Cost of One Scenario
• CostOne(C) Pw(C)frac(C)tset(C) (1-Pw(C))
• S (Prob. that Ci is the first category explored
  (Ki CostOne (Ci)))

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Cost Estimation

• S (Prob. that Ci is the first category explored
  (Ki CostOne(Ci)))
• The probability that Ci is the first category
  explored (i.e.,
• probability that the user explores Ci but none of
  C1 to C(i-1)), is
• (i-1) j
  1?(1-P(Cj)) P(Ci)
• Final CostOne Term
• CostOne(C) Pw(C)frac(C)tset(C) (1-Pw(C))
  i1nS(i-1) j 1?(1-P(Cj))
• P(Ci) (Ki CostOne (Ci)))
  (2)
• In case C is a leaf node then CostOne(C)
  frac(C)tset(C)

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Cost Estimation

• Using Workload to Estimate Probabilities
• P(C) and Pw(C) are needed for the CostOne(T) and
  CostAll(T)
• We use the aggregate knowledge of previous user
  behavior to estimate these probabilities
  automatically.
• Computing SHOWTUPLES Probability
• When a User explores the a non-leaf node C, there
  are two Choices SHOWCAT or SHOWTUPLES.
• SA(C) Subcategorizing attribute of C

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Cost Estimation

• SHOWCAT Probability
• Wi Workload Query.
• Ui User.
• If Ui has specified a selection condition on
• SA(C) in Wi, given a condition on SA(C) means
  user
• is interested in few values, if there is no
  condition it
• means user is interested in all values SA(C).

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Cost Estimation

• NAttr(A) the number of queries in the workload
  that contain
• selection condition on attribute A and N is
  the total number of queries in the workload.
• NAttr(SA(C))/N fraction of users that are
  interested in a few
• values of SA(C).
• SHOWCAT probability of C NAttr (SA(C))/N
• SHOWTUPLES probability of C (1- NAttr
  (SA(C))/N)

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Cost Estimation

• Computing Exploration Probability P(C)
• P(C) probability that user explores category C.
• P(C) P(User explores C User examines label of
  C).
• P(C) P(User explores C) / P(User examines label
  of C).
• User examines label if she explores the parent of
  label say C
• and chooses SHOWCAT for C.
• P(C) P( user explores C)/P(User explores C and
  chooses
• 
  SHOWCAT for
  C)
• P( user
  explores C)
• P( user explores C)P(User chooses SHOWCAT for
  Cuser
• 
  
  explores C)
• P(User chooses SHOWCAT for C' User explores C)
  is the
• SHOWCAT probability of C NAttr (SA(C))/N.
  
  

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Cost Estimation

• A user explores C if she, upon examining the
  label of
• C, thinks that there may be one or more tuples in
• tset(C) that is of interest to her.
• P(User explores C) / P(User explores C) is
  simply the probability that the user is
  interested in predicate
• label(C).
• So, P (user interested in predicate
  label C)
• P(C)
• NAttr(SA(C))/ N

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Cost Estimation

• CA(C) Categorizing attribute of C
• selection condition on CA(C) overlaps with the
  predicate
• label(C), it means that Ui is interested in
  the predicate label(C).
• NOverlap(C) number of queries in the workload
  Whose selection condition on CA(C) overlaps with
  label(C)
• P(User interested in predicate label(C))
  NOverlap (C)/N.
• So now we get
• 
  NOverlap (C)
• P(C)
• NAttr(CA(C))

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Algorithms
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Algorithms

• Now we can calculate the information overload
  cost for a given
• Tree, we can enumerate all possible category
  trees on R, and
• Chose the one with minimum cost. This will give
  the Cost-
• Optimal tree but will be expensive the sense that
  there will be
• large number of categorization trees possible.
• In order to solve this problem we need to
• Eliminate a subset of relatively unattractive
  attributes without considering any of their
  partitioning .
• For every attribute selected above, obtain a good
  partitioning efficiently instead of enumerating
  all the possible partitioning.

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Algorithms

• Reducing the Choices of Categorizing Attribute
• 1.)Eliminate the uninteresting attributes using
  the following
• simple heuristic if an attribute A occurs in
  less than a
• fraction x of the queries in the workload, i.e.,
  NAttr(A)/N lt x,
• we eliminate A. The threshold x will need to be
  specified by the
• system designer/domain expert.
• 2.) For attribute elimination, we preprocess the
  workload and
• maintain, for each potential categorizing
  attribute A, the
• number NAttr(A) of queries in the workload that
  contain
• selection condition on A.

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Algorithms

• Partitioning for Categorical Attributes
• In this paper only single value partitioning of
  R is considered.
• Consider the case where the user query Q contains
  a selection condition of the form A IN v1, ,
  vk on A.
• Example Neighborhood IN NeighborhoodRedmo
  nd,
• 
  NeighborhoodBellevue, etc.).
• Among the single-value partitioning, we want to
  choose the one with the minimum cost.
• Since the set of categories is identical in all
  possible single-value partitioning, the only
  factor that impacts the cost of a single valued
  partitioning is the order in which the categories
  are presented to the user.

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Algorithms

• The CostAll (T) is not affected by the ordering,
  so we will consider Only cost CostOne(T), now
  CostOne(T) is minimum when categories are
  presented in increasing order of 1/(P(Ci)
  CostOne(Ci).
• Heuristic to present the categories in decreasing
  order of P(Ci).
• P(Ci) NOverlap (Ci)/NAttr(A) , as Ci corresponds
  to a single value
• NOverlap (Ci) is the number of queries in the
  workload, whose selection condition on A contains
  vi in the IN Clause
• To obtain the partitioning we simply sort the
  values in IN clause in decreasing order of
  occ(vi).

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Algorithms
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Algorithms

• Partitioning for Numeric Attributes
• Let Vmin and Vmax be the minimum and maximum
  values that the
• tuples in R can take in attribute A.
• Let us consider a point v (Vmin lt v lt Vmax). If a
  significant number
• of query ranges in the workload begin or end at
  v, it is a good point
• to split as the workload suggests that most
  users would be interested
• in just one bucket,
• If none of them begin or end at v, hence v is not
  a good point to split, if we partition the range
  into m-buckets then (m-1) points should be
• selected where queries begin or end
  splitpoints.
• The splitpoints are not the only factors
  determining cost, the other
• factor is the number of tuples in each bucket.
• This kind of heuristic will not give best
  partitioning in the sense of cost.

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Algorithms

• Let us consider the point v
• again (Vmin lt v lt Vmax). Let
• startV and endV denote the
• number of query ranges in the
• Workload starting and ending
• at v respectively. We use
• SUM (startV, endV) as the
• goodness score of the point
• v.

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Algorithms

• Multilevel Categorization
• ALGORITHM
• 1.For multilevel categorization, for each level
  l, we need to determine the
• categorizing attribute A and for each category C
  in level (l-1), partition the
• domain of values of A in tset(C) such that the
  information overload is
• minimized.
• 2.The algorithm creates the categories level by
  level all categories at level
• (l-1) are created and added to tree T before any
  category at level l. S denote
• the set of categories at level (l-1) with more
  than M tuples.
• 3.For each such candidate attribute A, we
  partition each category C in S
• using the partitioning for Categorical
  Attributes and Numerical attributes
• 4. Compute the cost of the attribute-partitioning
  combination for each
• candidate attribute A and select the attribute a
  with the minimum cost. For
• each category C in S, we add the partitions of C
  based on a to T.
• 5. This Completes the node creation at level l.

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Experimental Evaluation

• Evaluation is done on the following
• Evaluate the accuracy of cost models in modeling
  information overload.
• Evaluate our cost based categorization algorithm
  and compare them with categorization that do not
  consider such cost models.
• Database MSN HouseHome
• M 20
• All Experiments are a conducted on Compaq Evo
  W8000 1.7Ghz CPU
• 768MB RAM, running on Windows XP.
• Dataset for both the experiments Single table
  called ListProperty , it
• contains 1.7 million rows.
• Workload comprises 176,262 query strings
  representing searches
• conducted by home buyers on MSN House Home
  website.
• In both the studies papers cost based is
  compared to two techniques
• No-Cost and Attr-Cost
• No-Cost it uses the same level by
  categorization but categorizing attr-
• -butes at each level arbitrarily (without
  replacement).

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Experimental Evaluation

• Attr-Cost Attr-cost technique selects the
• attribute with the lowest cost as the
• categorizing attribute at each level but
• considers only those partitioning considered
• by the No cost technique.
• Simulated User-Study
• Due to the difficulty of conducting a large-
• scale real-life user study, we develop a novel
• way to simulate a large scale user study. We
• pick a subset of 100 queries from the
• workload and imagine them as user
• explorations, Workload Query W is referred
• to as Synthetic Exploration.
• estimated (average) cost CostAll(T)
• actual cost CostAll(W,T) of exploration
• 8 Mutually disjoint subsets of 100 synthetic
  explorations are considered.
• Figure is Correlation between actual cost and
• Estimated Cost.

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Experimental Evaluation

• Figure on the left is Cost of various techniques
  for 8
• Subsets.
• Figure on the right is Pearson's correlation
  between the
• estimated cost and actual cost.

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Experimental EvaluationReal Life User study

• Tasks
• 1. Any neighborhood in Seattle/Bellevue, Price lt
  1 Million.
• 2. Any neighborhood in Bay Area Penin
• /SanJose, Price between 300K and 500K
• 3. 15 selected neighborhoods in NYC
• Manhattan, Bronx, Price lt 1 Million
• 4. Any neighborhood in Seattle/Bellevue, Price
  between 200K and 400K, Bedroom Count between 3
  and 4.

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Experimental EvaluationReal Life User study

• Figure on the left is Average Cost (no. of items
  examined till she finds all the relevant tuples)
  of various techniques
• Figure on the right is Average number relevant
  tuples found by users for the various techniques.

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Experimental EvaluationReal Life User study

• Figure on the left is Average Normalized Cost
  (items examined by user/ relevant tuple found)
  of various techniques
• Figure on the right is Average Cost (till she
  finds the first relevant tuple found) of various
  techniques

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Experimental EvaluationReal Life User study

• Figure on the left Results of the post study
  survey
• Figure on the right Average execution time of the
  cost
• based categorization algorithm.

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Conclusion

• This paper gives a solution for the problem
  Information
• Overload by purposing the automatic
  categorization of Query
• results. The solution is to dynamically generate
  a labeled,
• hierarchical category structure the user can
  determine
• whether a category is relevant or not by
  examining simply its
• label and explore only the relevant categories,
  thereby
• reducing information overload.

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Thank You