Harikrishnan Karunakaran Sulabha Balan

Keyword Searching and Browsing in Databases using BANKS

Gaurav Bhalotia, Arvind Hulgeri, Charuta Nakhe, Soumen Chakrabarti, S. Sudarshan

CSE 6339

Introduction

Database and Query Model◦ Informal Model◦ Formal Model◦ Query and Answer Model

Searching for the Best Answers◦ Backward Expanding Search Algorithm

Browsing through BANKS

Experience and Performance

Related Work

Conclusion

Outline

With the onset of the web, number of users needing to access online databases have increased

Search engines have popularized use of unstructured querying which just needs the user to type in the keyword and follow links

Same methodology cannot be used in querying databases as knowledge of schema and querying language like SQL is needed

Keyword searching will not also work on datatbases because the data is usually spread across tables/tuples due to normalization

Introduction

Browsing ANd Keyword Searching

Enables Keyword-Based search on Relational Databases along with Data and Schema Browsing

User interacts with data through typing keywords, following hyperlinks and using controls made available.

Absolutely no knowledge of querying or programming languages required of the user

BANKS

BANKS contd…

BANKS contd… Makes Joins Implicit and Transparent

Incorporates Notions of Proximity and Prestige

Methods to publish relational data which would otherwise remain invisible on the web are provided

Creates hierarchical and graphical views of data with hyperlinks to navigate through them

Database and Query Model

Schema

Database Fragment Snapshot

Answer to query should be sub-graph connecting nodes matching the keywords

Central node that connects all keyword nodes is Information Node and tree is Connection Tree. Foreign Key Dependencies constitute edges in the graph

Edges (References) are given weights according to its type

Weight of the tree is proportional to total of its edge weights and relevance is inversely proportional to its weight

Informal Model

To obtain model with edges directed away from information node and preserve directionality we make use of backward edges

Backward Edge assigned weight proportional to in-degree

Information Node is selected from certain sets of nodes in the graph

Backward Edges ensure that tree is rooted at the Information Node

To avoid problem of “hubs”, edges connecting popular nodes are given a higher weight thus lowering proximity

Informal Model contd…

Informal Model contd…

Concept of Node weights introduced to include Prestige Rankings

Nodes having more pointers given higher prestige

In BANKS node prestige is assigned based on in-degree of the node

Node weights and Tree Weights combined to obtain relevance score

Formal Database Model Each tuple Τ has a corresponding node uτ

Each node u has a node weight N(u) depending upon the prestige of the node

Between each pair of related tuples T1 & T2 , graph contains edge between uτ1 to uτ2 and back edge from uτ2 to uτ1

Similarity between two relations R1 and R2 depends upon the type of link from R1 to R2 and is set to infinity if R1 does not refer to R2

Edge weights

Depending upon importance of the link we set a value to the edge. Default value is 1

The weight of the directed edge(u, v) depends on factors: If (u, v) exists but (v, u) does not, assign the weight s(R(u),R(v)) to (u,

v) If (u, v) does not exist and (v, u) does, assign the weight INv (u) s(R(v),

R(u)) to (u, v) where INv is the indegree of u contributed by the tuples belonging to relation R(v)

If both (u, v) and (v, u) exist in the graph , assign the weight as the minimum of two values

min{s(R(u),R(v)) , INv (u) s(R(v), R(u))

Formal Database Model

QueryA set of keywords e.g.{k1,k2,…kn}A set of nodes Si = {S1,S2,…Sn}Locate nodes matching search terms t1,t2,…tn

Answer Model A rooted directed tree connecting keyword nodes (at

least one node from Si).Note: Tree may also contain nodes not in any Si , Steiner

TreeRelevance score of an answer tree

Combination of its nodes and its edge weight presented in decreasing order

Query and Answer Model

Calculating Relevance Score involves adjustment of both node weights and edge weights along with a factor to control individual weight variations

Node weights Scaled to Nmax and depressed using log Nscore(v) = N(v)/ Nmax or log(1+N(v)/Nmax Overall Nscore taken to be average of node scores

Edge Weights Normalized Escore(e) obtained by diving edge weight by minimum edge weight Escore(e) = log(1+w(e)/wmin) Overall Edge Score = 1/(1 + Σe Escore(e))

Combination of Overall Edge Score and Node Score Additive : (1-λ)Escore + λNscore Multiplicative : Escore * Nscoreλ

Query and Answer Model

We have to use not just the tree with the highest relevance score but also those with high scores

Answers have to be generated incrementally so that the user are provided with the ‘best’ answers at the beginning

Resultant Graph is assumed to fit in memory since only Row IDs and index to map RowIDs to nodes in the graph need to be stored by us.

Searching for the Best Answers

Incrementally computes search results

Start at leaf nodes each containing a query keyword

Run concurrent single source shortest path algorithm from each such node

Traverses the graph edges backwards Confluence of backward paths identify answer tree roots

Output a node whenever it is on the intersection of the sets of nodes reached from each keyword

Answer trees may not be generated in relevance order

Insert answers to a small buffer (heap) Output highest ranked answer from buffer to user when buffer is full

Backward Expanding Search

Backward Expanding Search

Backward Expanding Search

Searching for Best Answers Model (Query : Roy Sudarshan)

Backward Expanding Search Due to the graphs being Steiner Trees lot of time is

spent doing wasteful exploration of the graph

As keyword nodes increase, the feasibility of the algorithm decreases

Connection Trees are only approximately sorted in their increasing order of weights

Node weights are not considered, hence trees may not be produced in exact decreasing order of relevance

BANKS system providesA rich interface to browse data stored in a relational

database

Automatically generates browsable views of database relations and query results

Schema browsing and data browsing

A hyperlink to the referenced tuple

Browsing through BANKS

Browsing through BANKS contd… Functionalities

Columns can be projected away

Selections can be imposed on columns

Joins can be performed with foreign key columns by joining them with referencing tables

Results can be grouped by on columns which returns only distinct values in column being displayed

Sorting can be done on columns

Sample Browsing

Cross Tabs

Group By Template to view Data hierarchially

Folder Views modeled after the folder view supported by Windows Explorer etc.

In the form of bar chart, line chart or pie chart with HTML image maps to embed hyperlinks in the graphics

Templates

Datasets of varying sizes have been tested

No agreed upon benchmarks for Ranking Algorithms in this domain

System was found to return the most intuitive answers

Performance Evaluation

Ideal answers were obtained using different queries

Compute absolute value of rank difference of the ideal answers with rank in the answers for given parameter setting

Sum of rank differences gives the raw error score for that parameter

We map error scores against λ and log-scaling of edge weights

Evaluating Error Score

Error Scores

Setting λ = 0.2 produced best results while λ = 1 produced worst with error scores of around 15

Log scaling of edge weight is important as otherwise back-edges from popular nodes would result in correct answers getting low relevance scores

Additive or Multiplicative combination has no effect on ranking

Node weights were abandoned as log-scaling and no log scaling produced same ranking

Observations

Effective when using queries matching non-metadata keywords

Brings to light data that might not be readily available on the web to the non technical user

Higher the no. of keywords, the less useful backward-expanding search algorithm becomes

Over reliance on Java can at times cause slow down of application

Conclusions