RELEVANT UPDATED DATA RETRIEVAL ARCHITECTURAL MODEL FOR CONTINUOUS TEXT EXTRACTION

Sundarapandian et al. (Eds) : ACITY, AIAA, CNSA, DPPR, NeCoM, WeST, DMS, P2PTM, VLSI - 2013
pp. 21–27, 2013. © CS & IT-CSCP 2013 DOI : 10.5121/csit.2013.3403
RELEVANT UPDATED DATA RETRIEVAL
ARCHITECTURAL MODEL FOR
CONTINUOUS TEXT EXTRACTION
Srivatsan Sridharan1
, Kausal Malladi1
and Yamini Muralitharan2
1
Department of Computer Science,
International Institute of Information Technology - Bangalore, India.
{vatsansridharan,kausalmalladi}@gmail.com
1
Department of Software Engineering,
International Institute of Information Technology - Bangalore, India.
{yams25}@gmail.com
ABSTRACT
A server, which is to keep track of heavy document traffic, is unable to filter the documents that
are most relevant and updated for continuous text search queries. This paper focuses on
handling continuous text extraction sustaining high document traffic. The main objective is to
retrieve recent updated documents that are most relevant to the query by applying sliding
window technique. Our solution indexes the streamed documents in the main memory with
structure based on the principles of inverted file, and processes document arrival and expiration
events with incremental threshold-based method. It also ensures elimination of duplicate
document retrieval using unsupervised duplicate detection. The documents are ranked based on
user feedback and given higher priority for retrieval.
1. INTRODUCTION
Data intensive applications such as electronic mail, news feed, telecommunication management,
automation of business reporting etc raise the need for a continuous text search and monitoring
model. In this model the documents arrive at the monitoring server as in the form of a stream. Each
query Q continuously retrieves, from a sliding window of the most recent documents, the k that is
most similar to a fixed set of search terms. Sliding window. This window reflects the interest of
the users in the newest available documents. It can be defined in two alternative ways. They are a)
count-based window contains the N most recent documents for some constant number N, b) time-
based window contains only documents that arrived within last N time units. Thus, although a
document which may be relevant to a query, it is ignored, because it may not satisfy the time and
count constraints of the user. Incremental threshold method. The quintessence of the algorithm is
to employ threshold-based techniques to derive the initial result for a query, and then continue to
update the threshold to reflect document arrivals and expirations. At its core lies a memory-based
index similar to the conventional inverted file, complimented with fast updated techniques.
MapReduce technique. MapReduce is a powerful platform for large scale data processing. This
technique involves two steps namely a) map step: The master node takes the input, partitions it up

22 Computer Science & Information Technology (CS & IT)
into smaller sub-problems, and distributes them to worker nodes. A worker node may do this again
in turn, leading to a multi-level structure. The worker node processes the smaller problem, and
passes the answer back to its master node, b) reduce step: The master node then collects the
answers to all the sub-problems and combines them in some way to form the output – the answer
to the problem it was originally trying to solve. Unsupervised duplicate detection. [3]
The problem
of identifying objects in databases that refer to the same real world entity, is known, among others,
as duplicate detection or record linkage. Here this method is used to identify documents that are all
alike and prevent them from being prepared in the result set. Our paper also focuses on ranking the
documents based on user feedback. The user is allowed to give feedback for each document that
has been retrieved. This feedback is used to rank the document and hence increase the probability
of the document to appear in the sliding window.
Visual Web Spider is a fully automated, multi-threaded web crawler that allows us to index and
collect specific web pages on the Internet. Once installed, it enables us to browse the Web in an
automated manner, indexing pages that contain specific keywords and phrases and exporting the
indexed data to a database on our local computer in the format of our choice. We want to collect
website links to build our own specialized web directory. We can configure Visual Web Spider
automatically. This program’s friendly, wizard-driven interface lets us customize our search in a
step-by-step manner. To index relevant web pages, just follow this simple sequence of steps.
After opening the wizard, enter the starting web page URL or let the program generate URL links
based on specific keywords or phrases. Then set the crawling rules and depth according to your
search strategy. Finally, specify the data you want to index and your project filename. That’s
pretty much it. Clicking on ‘Start’ sets the crawler to work. Crawling is fast, thanks to multi-
threading that allows up to 50 simultaneous threads. Another nice touch is that Visual Web Spider
can index the content of any HTML tag such as: page title (TITLE tag), page text (BODY tag),
HTML code (HTML tag), header text (H1-H6 tags), bold text (B tags), anchor text (A tags), alt
text (IMG tag, ALT attribute), keywords, description (META tags) and others. This program can
also list each page size and last modified date.
Once the web pages have been indexed, Visual Web Spider can export the indexed data to any of
the following formats: Microsoft Access, Excel (CSV), TXT, HTML, and MySQL script.
1.1 Key Features
A Personal, Customizable Web crawler. Crawling rules. Multi-threaded technology (up to 50
threads). Support for the robots exclusion protocol/standard (Robots.txt file and Robots META
tags);Index the contents of any HTML tag. Indexing rules; Export the indexed data into Microsoft
Access database, TEXT file, Excel file (CSV), HTML file, MySQL script file; Start crawling
from a list of the URLs specified by user; Start crawling using keywords and phrases; Store web
pages and media files on your local disk; Auto-resolve URL of redirected links; Detect broken
links; Filter the indexed data;
2. EXISTING SYSTEM
Drawbacks of the existing servers that tend to handle the heavy document traffic are: Cannot
efficiently monitor the data stream that has highly dynamic document traffic. The server alone
does the processing hence it involves large amount of time consumption. In case of continuous text
search queries and extraction every time the entire document set has to be scanned in order to find

Computer Science & Information Technology (CS & IT)
the relevant documents. There is no confirmation that duplicate documents are not retrieved for the
given query. A large amount of documents cannot be stored in the main memory as it involves
large amount of CPU cost.
Naïve solution: The most straightforward approach to evaluate the
above is to scan the entire window
intervals, compute all the document scores, and report the
incurs high processing costs due
3. PROPOSED SYSTEM
3.1 Problem Formulation
In our model, a stream of documents flows into a central server. The user registers text queries at
the server, which is then responsible for continuously monitoring/reporting their results. As in
most stream processing systems, we store all the data in main memory in order to cope with
frequent updates, and design our methods with the primary goal of minimizing the CPU cost.
Moreover it is necessary to reduce the work load of the monitoring server.
3.2 Proposed Solution
In our solution we use the MapReduce technique in order to reduce the work load of the central
server, where the server acts as the master node, which splits up the processing task to several
worker nodes. The number of worker nodes, which have been assign
depends on the nature of query that has been put up by the user. Here the master node, upon
receiving a query from the user, assigns the workers to find the relevant result query set and
return the solution to the master node. The
the workers, integrates the results to produce the final result set for the given query. This can be
viewed schematically in the following
incremental threshold algorithm
for the given query. The overall system architecture can be viewed as in the following
Fig. 1. System Architecture for the proposed Re
ts. There is no confirmation that duplicate documents are not retrieved for the
The most straightforward approach to evaluate the continuous queries defined
above is to scan the entire window contents D after every update or in fixed time
mpute all the document scores, and report the top-k documents. This method
igh processing costs due to the need for frequent re computations from scratch.
YSTEM
3.1 Problem Formulation
responsible for continuously monitoring/reporting their results. As in
eover it is necessary to reduce the work load of the monitoring server.
worker nodes. The number of worker nodes, which have been assigned the processing task,
return the solution to the master node. The master node, after receiving the partial solutions from
viewed schematically in the following Fig.1. Each worker/slave node is responsible uses the
incremental threshold algorithm for computing the result set of k relevant and recent documents
for the given query. The overall system architecture can be viewed as in the following
System Architecture for the proposed Relevant Updated Architecture Model.
23
ts. There is no confirmation that duplicate documents are not retrieved for the
tinuous queries defined
contents D after every update or in fixed time
documents. This method
to the need for frequent re computations from scratch.
responsible for continuously monitoring/reporting their results. As in
ed the processing task,
master node, after receiving the partial solutions from
Each worker/slave node is responsible uses the
for computing the result set of k relevant and recent documents
for the given query. The overall system architecture can be viewed as in the following Fig.2
levant Updated Architecture Model.

Fig. 2. A data Retrieval system for continuous data extraction technique using MapReduce.
Each element of the input stream comprises of a document d, a
document arrival time, a composition list
term t belonging to T in the document and wdt is the frequency of the term in the document d.
The notations in this model are as follows in Fig 3.
Fig. 3. A Detailed list of the notations used in the paper for the proposed system.
The worker node maintains an inverted index for each term t in the document. With the inverted
index, a query Q is processed as follows: the inverted lists for the terms t belongi
scanned and the partial wdt scores of each encountered document d are accumulated to produce
S(d/Q). The documents with the highest scores at the end are returned as the result.
A data Retrieval system for continuous data extraction technique using MapReduce.
Each element of the input stream comprises of a document d, a unique document identifier, the
composition list. The composition list contains one (t, wdt) pair for each
The notations in this model are as follows in Fig 3.
A Detailed list of the notations used in the paper for the proposed system.
index, a query Q is processed as follows: the inverted lists for the terms t belongi
S(d/Q). The documents with the highest scores at the end are returned as the result.
A data Retrieval system for continuous data extraction technique using MapReduce.
unique document identifier, the
The composition list contains one (t, wdt) pair for each
A Detailed list of the notations used in the paper for the proposed system.
index, a query Q is processed as follows: the inverted lists for the terms t belonging to Q are

Computer Science & Information Technology (CS & IT) 25
3.3 Incremental Threshold Algorithm
Fig.3 represents the data structures that have been used in this system. The valid documents D are
stored in a single list, shown at the bottom of the figure. Each element of the list holds the stream
of information of document (identifier, text content, composition list, arrival time). D contains the
most recent documents for both count-based and time-based windows. Since documents expire in
first-in-first-out manner, D is maintained efficiently by inserting arriving documents at the end of
the list and deleting expiring ones from its head. On the top of the list of valid documents we
build an inverted index. The structure at the top of the figure is the dictionary of search terms. It
is an array that contains an entry for each term t belonging to T. The dictionary entry for t stores a
pointer to the corresponding inverted list Lt. Lt holds an impact entry for each document d that
contains t, together with a pointer to d’s full information in the document list. When a document
d arrives, an impact entry (d, wdt) (derived from d’s composition list) is inserted into the inverted
list of each term t that appears in d. Likewise, the impact entries of an entries of an expiring
document are removed from the respective inverted lists. To keep the inverted lists sorted on wdt
while supporting fast (logarithmic) insertions and deletions.
Initial Top-k Search: When a query is first submitted to the system, its top-k result is computed
using the initial search module. The process is an adaptation of the threshold algorithm. Here, the
inverted lists Lt of the query terms play the role of the sorted attribute lists. Unlike the original
threshold algorithm, however we do not probe the lists in a round robin fashion. Since the
similarity function associates different weights wQt with the query specifically, inspired by [4],
we probe the list Lt with the highest ct=wQt.wdnxtt value, where dnxt is the next document in Lt.
The global threshold gt, a notion used identically to the original algorithm, is the sum of ct values
for all the terms in Q. Consider query Q1 with search string “red rose” and k=2. Let term
t20=”red” and t11=”rose”. First the server identifies the inverted lists L11 and L20 (using the
dictionary hash table), and computes the values c11=wQ1t11.wd7t11 and c20=wQ1t20.wd6t20. In
iteration 1, since c20 is larger, the first entry of L20 is popped; the similarity score of the
corresponding document, d6, is computed by accessing its composition list in D and inserted into
the tentative R. c20 is then updated to impact entry which is above local threshold, but we would
still include it in R as unverified entry.
The algorithm is as follows,
Algorithm Incremental Threshold with Duplicate Detection
(Arriving dins, Expiring ddel)
1: Insert document dins into D (the system document list)
2: for all terms t in the composition list of dins do
3: for all documents in Lt
4: for all terms t in dins
5: Compute unique (dins)
6: wdinst != wdnxtt
7: Insert the impact entry of dins into Lt
8: Probe the threshold tree of Lt
9: for all queries Q where wdinst > =localThreshold do
10: if Q has not been considered for dins in another Lt then
11: Compute S (dins/Q)
12: Insert dins into R
13: if S(dins/Q)>= old Sk then
14: Update Sk (since dins enters the top-k result)

15: Keep rolling up local thresholds while r <= Sk
16: Set new τ as influence threshold for Q
17: Update local thresholds of Q
18: Delete document ddel from D (the system document list)
19: for all terms t in the composition list of ddel do
20: Delete the impact entry of ddel from Lt
21: Probe the threshold tree of Lt
22: for all queries Q where wddelt >= localThreshold do
23: if Q has not been considered for ddel in another Lt then
24: Delete ddel from R
25: if S(ddel/Q) >= old Sk then
26: Resume top-k search from local thresholds
27: Set new τ as influence threshold for Q
28: Update local thresholds of Q
After constructing the initial result set R using the above algorithm, only the documents that have
a score higher than or equal to the influence threshold t(tow) are verified. The main key point is
that no duplicate documents from the part of the result set R. This is ensured using unsupervised
duplication detection. The idea of unsupervised learning for duplicate detection has its roots in
the probabilistic model proposed by Fellegi and Sunter. When there is no training data to compute
the probability estimates, it is possible to use variations of the Expectation Maximization
algorithm to identify appropriate clusters in the data.
Fig. 3. Data Structures used for Incremental Threshold Algorithm

Computer Science & Information Technology (CS & IT) 27
3. CONCLUSION
In this paper, we study the processing of continuous text queries over document streams. These
queries define a set of search terms, and request continual monitoring of a ranked list of recent
documents that are most similar to those terms. The problem arises in a variety of text monitoring
applications, e.g., email and news tracking. To the best of our knowledge, this is the first attempt
to address this important problem. Currently, our study focuses on plain text documents. A
challenging direction for future work is to extend our methodology to documents tagged with
metadata and documents with a hyperlink structure, as well as to specialized scoring mechanisms
that may apply in these settings.
REFERENCES
[1] B.Babcock, S. Babu, M.Datar, R.Motwani, and J.Widom, 2002, “Models and Issues in Data
Streaming System” PODS’02, 1-16.
[2] J.Zobel and A.Moffat, July 2006, “Inverted Files for Text Search Engines”, Computing Surveys,
vol.38, o.2, p1-55.
[3] VNAnh and A.Moffat, 2002, “Impact Transformation: Effective Efficient Web Retrieval”, Int’l ACM
SIGIR conf. Research and Development in Information Retrieval.
[4] V.N.Anh, O.de Krestser, and A. Moffat, 2001, “Vector-Space Ranking with Effective Early
Termination”, ACM SIGIR conf. Research and Development in Information Retrieval.
[5] Y. Zhang and J.Callan, 2001 “Maximum Likelihood Estimation for Filtering Tresholds,” ACM SIGIR
conf. Research and Development in Information Retrieval.
[6] M. Persin, J. Zobel and R. Sacks-Davis, 1996, “Filtered Document Retrieval with Frequency_Sorted
Indexes”, J.Am.Soc for Information Science, vol.47, no.10.

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