Efficient Top-k Searching According to User Preferences Based on Fuzzy Functions with Usage of Tree-Oriented Data Structures Matúš Ondreička Superised by Prof. Jaroslav Pokorný Faculty of Mathematics and Physics Department of Software Engineering Charles University in Prague Czech Republic
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Matúš Ondreička Superised by Prof. Jaroslav Pokorný Faculty of Mathematics and Physics
Efficient Top-k Searching According to User Preferences Based on Fuzzy Functions with Usage of Tree-Oriented Data Structures. Matúš Ondreička Superised by Prof. Jaroslav Pokorný Faculty of Mathematics and Physics Department of Software Engineering Charles University in Prague - PowerPoint PPT Presentation
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Efficient Top-k Searching According to User Preferences
Based on Fuzzy Functions with Usage of Tree-Oriented Data Structures
Matúš OndreičkaSuperised by Prof. Jaroslav Pokorný
Faculty of Mathematics and PhysicsDepartment of Software Engineering
Charles University in PragueCzech Republic
Matúš Ondreička 2VLDB 2011 PhD Workshop
Research - outline introduction
top-k problem, user preferences, fuzzy functions related work technical solutions
Tree-Oriented Data Structures set of B+-trees multidimensional B+-tree multidimensional B+-tree with lists
MD-algorithm, MXT-algorithm experiments, current results motivation of future research
Matúš Ondreička 3VLDB 2011 PhD Workshop
Top-k problem top-k searching
the (few) best k objects with more attributes k objects with the highest ratting
according to user preferences based on fuzzy functions
efficient top-k searching without accessing all the objects allow the full support of model of user preferences
local preferences global preferences
Matúš Ondreička 4VLDB 2011 PhD Workshop
Model of user preferences local preferences
objects are preferred according to one attribute an attribute's domain is continuous
modeled with an fuzzy function fU(x): xA → [0, 1]
an attribute's domain is discrete evaluating of each value
ACER := 0.6, APPLE := 1.0, DELL := 0.9, SONY := 0.8 global preferences
objects are preferred according more attributes modeled with an aggregation function
@U(x): ( f1U(x), ..., fm
U(x) ) → [0, 1]
e.g. weighted average
fU(x)100% 1
xA0€ 1000€
w1 . f1U(x) + ... + wm . fm
U(x)w1 + ... + wm
@U(x) =
0% 0
Matúš Ondreička 5VLDB 2011 PhD Workshop
Motivation and related work XML, multimedia, the Web, etc. relational databases
Ilyas, Beskales, Soliman: A survey of top-k query processing techniques in relational database systems. 2008.
ranking functions query optimalization
Fagin's algorithms Fagin, R., Lotem, A., Naor, M.: Optimal aggregation algorithms for
middleware. Journal of Computer and System Sciences 66, 2003. only support of a monotone ranking functions based on sorted lists no supporting of local user preferences
BASIC MOTIVATION FOR OUR RESEARCH
Matúš Ondreička 6VLDB 2011 PhD Workshop
Usage of B+-tree local user preference
by fuzzy function on monotonous interval
moving in leaf level ‘’ways’’ in leaf level continuously on all ‘’ways’’ comparing objects on
different ‘’ways’’ choosing the biggest on all
the ‘’ways’’ obtaining objects
during the computation of algorithm
with ratings in descending order
by fuzzy function fU
0.9 1.0
QE
R TYU
SD FGH
KCNM
0.2 0.5 0.8
0.6 0.7 0.80.3 0.4 0.50.0 0.1 0.2
0.5 0.6 0.7 0.8 0.9 10.40.30.20.10
1
0
w5w1 w2 w3 w4
Matúš Ondreička 7VLDB 2011 PhD Workshop
Fagin's algorithms TA (threshold algorithm) and NRA (no random access)
searches the best k objects according to monotone aggregate function @ without accessing all objects
preconditions a set of objects X with values of m attributes A1, ..., Am objects from the set X are stored in m lists L1, ..., Lm lists contain pairs (x, ax) lists are sorted in descending order monotone aggregation function @
multi-user solution lists are based on B+-tree algorithm can get pairs (x, fU(x))
from B+-tree sequentially in descending order according to
MDB-tree allows to index set of objects by m > 1 attributes in one data structure m levels, values of one attribute are stored in each level nodes are B+-trees, whose leaf nodes are linked in two directions
An example of results implemented top-k algorithms
TA-algorithm, MD-algorithm, MXT-algorithm using lists based on B+-trees implementation in Java data structures have been tested in memory (not on disk)
tests results the number of obtained objects
real data 8 822 flats for rent in Prague ||dom(District)|| = 10 ||dom(Type)|| = 10 ||dom(Area)|| = 229 ||dom(Price)|| = 411
real user's preferences user prefers flats of some types in specific districts, smaller prices and bigger areas
Matúš Ondreička 13VLDB 2011 PhD Workshop
Motivation, future research improvements of performance of algorithms
heuristics to monitor a distribution of the key values in nodes
improvement of data structures. automatic arrangement levels in MDB-tree with lists, manage empty values
parallel computing in MXT-algorithm construction, instances of TA-algorithm would be computed concurrently
different models of user preferences attribute dependencies between more attributes similarity measures
to find k objects most similar to an object can be user preference user feedback
After running of first top-k query user tune his/her preferences and execute next top-k query different data models
very large data sets tree-oriented data structure allow to dynamise the environment while solving a top-k problem
data streams tree-oriented data structure as a sliding window
approximations, uncertain data, heterogeneous data web environment