INTEGRATING NETWORK
STORAGE INTO INFORMATION
RETRIEVAL APPLICATIONS
Svetlana Y. Mironova
The University of Tennessee,
Knoxville
Spring 2003
Topics of Discussion
Motivation
 General Text Parser (GTP)
 Network Storage Stack
 GTP with Network Storage
 Implementation Challenges
 Performance
 Future Work
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Motivation
Amount of textual-based information stored
on our computers and on the Web is rapidly
accumulating.
 Researchers and scientists need storage to
run simulations and store outputs.
 Data mining and information retrieval
professionals need a tool capable of creating
an index from a document collection, storing
it on the network and sharing with others.
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General Text Parser (GTP)
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Two modules: GTP and GTPQUERY
Text/document parsing and indexing
Construct sparse matrix data structures
Create vector-space model where documents
and queries are vectors in low-dimensional
subspace
Term-by-document matrix defines
relationships between docs and distinct terms
Underlying model is Latent Semantic Indexing
(LSI)
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Versions of GTP
C++ (original)
 Parallel C++ using MPI (for SVD
computation)
 Java (GUI recently developed)
 Solaris (Unix), Linux in C++
 Parallel only on Solaris
 Solaris, Linux, Mac OS X in Java
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GTP Process
Filter documents (optional)
 Create database of keys, IDs and
weights
 Perform matrix decomposition (SVD) on
the term-by-document matrix
 Clean up
 Write out summary
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Query Process
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Filter queries (optional)
Parse first query
Generate query vector
Scale query vector by singular values
(optional)
Perform cosine matching
Write results to file for this query
Repeat for more queries
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Network Storage Stack
Framework for storing and transferring
data over network
 Modeled after Internet Protocol (IP)
Stack
 Designed to add storage resources to
the Internet in a sharable and scalable
manner
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Network Storage Stack
Applications (GTP, etc)
Logistical File System
Logistical Tools
L-Bone
exNode
IBP
Local Access
Physical Layer
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IBP
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Internet Backplane Protocol
Foundation of Network Storage Stack
Share resources across networks
Use of local storage to create global storage
service
Echoes advantages of IP: abstraction of
datagram delivery, scalability, simple fault
detection (discard faulty datagrams)
Temporary and “unreliable”
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IBP Client Calls
Allocate
 Store
 Load
 Copy
 Mcopy
 Manage
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exNode
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Hard to manage IBP capabilities
exNode automates it
exNodes are pointers to IBP allocations
Allows to create network files from unreliable
IBP allocations, with stronger properties
(fault-tolerance, longer duration, etc.)
Two major components: metadata and
mappings
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L-Bone
The Logistical Backbone
 Resource discovery service
 Maintains list of public depots and
metadata about them
 Uses Network Weather Service (NWS)
to monitor throughput between depots
 http://loci.cs.utk.edu/lbone
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LoRS
Logistical Runtime System
 Automate finding of IBP depots via LBone, creation and management of IBP
capabilities and exNodes
 C API and command line interface tool
set
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LoRS Functions
Upload
 Download
 Augment
 Trim
 Refresh
 List
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GTP with Network Storage
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Creating an index is a dynamic process
Large document collection => large output
files => require lots of storage space
Need to share produced results with others
(across the globe)
If not satisfied with result – stored files will
go away automatically
If happy with collection – can either store on
IBP longer or store locally (burn on CD, etc)
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GTP and Upload
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GTP parses the collection
GTP creates output files (keys and output )
Files are uploaded to remote network (IBP)
Upload requires some information from the
user (optional)
Information helps optimize performance
Capabilities are returned in the form of XML
files (.xnd extension)
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GTP and Upload (contd)
Location (Null, hostname, zip, state,
city, country, airport)
 Duration in days
 Fragments
 Copies
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Download and GTPQUERY
Files keys and output are downloaded
using information from .xnd files
 Download is multithreaded
 Adaptive algorithm: takes into account
throughput to the client
 “Faster” depots provide more blocks of
data
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Download + GTPQUERY
5K
100
Representation of the binary file output for 5K collection
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Implementation Challenges
GTP in Java, while LoRS tools in C
 Go through server (first xnd_server,
then lors_server)
 Adapt to changes – both GTP and LoRS
tools are constantly evolving
 Threading to optimize performance
 User friendliness
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Performance
All results were achieved using the Java
version of GTP
 Three sub collections of FBIS (Foreign
Broadcast Information Service) were used to
produce benchmarks
 Server located in Tennessee
 Upload/download to/from Tennessee(TN),
California (CA), France (FR)
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Run Specifications
By default, GTP uses 100 SVD factors, i.e., all
term and document vectors are of length 100
 The weighting scheme used was log entropy
 For the query only the first 15 singular triplets
were used
 Three queries were used on each collection:
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Yugoslavia Croatia Bosnia-Herzegovina
Russia embassy FIS
Nissan Motor
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FBIS 5K
FBIS 5K
FBIS 5K
85
300
14
50
284
284
Seconds
Seconds
400
200
284
100
0
FR
CA
TN
60
50
40
30
20
10
0
6.4
49
FR
6.4
19.4
CA
TN
Location
GTP
6.4
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Location
Upload
Download
GTP + Upload
GTPQUERY
Download + GTPQUERY
Name
Size
Docs
Terms output keys
FBIS 5K
17.8 MB
5,000
22,558
11 MB
2.78 MB
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FBIS 10K
FBIS 10K
FBIS 10K
600
157
20
19
548
548
Seconds
Seconds
800
400
200
548
0
FR
CA
100
80
60
40
20
0
21
71
FR
TN
21
30
21
21
CA
TN
Location
Location
GTP
Upload
Download
GTP + Upload
GTPQUERY
Download + GTPQUERY
Name
Size
Docs
Terms output keys
FBIS 10K
32 MB
10,000
31,667
18 MB
3.5 MB
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FBIS 20K
FBIS 20K
FBIS 20K
150
1500
23
270
23
78
1320
1320
1320
Seconds
Seconds
2000
1000
500
0
FR
CA
100
50
113
23
38
23
34
CA
TN
0
TN
FR
Location
GTP
Location
Upload
Download
GTP + Upload
GTPQUERY
Download + GTPQUERY
Name
Size
Docs
Terms output keys
FBIS 20K
63 MB
20,000
46,488
28 MB
5.8 MB
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Performance Analysis
GTP + Upload
GTP time is directly proportional to the
collection size
 Additional overhead for upload is not
significant compared to the total time
 Upload time depends on multiple factors:
location, network bandwidth, time of day, size
of file, number of copies requested, and
status of depots at the time of the upload
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Performance Analysis
Download + GTPQUERY
All “heavy-duty” preprocessing of the collection was
done by GTP
 Query process simply projects the query into the
term-by-document vector space
 Dimension of the vector space and number of factors
used affects query time
 Number of queries requested affects query time
 Download takes up greater portion of the total time
 Download is affected by location of fragments and
network conditions
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Future Work
Optimize Java performance
 Incorporate fully with GUI
 Incorporate network storage into the
other (C++, parallel) versions of GTP
 Streaming data directly while it is
generated?
 Avoid local file generation
 User friendliness
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