Alexander Vodyaho & Nataly Zhukova — Implementation of Agile Concepts in Recommender Systems for Data Processing and Analyses

Implementation of Agile Concepts in
Recommender Systems for Data
Processing and Analyses
Alexander Vodyaho,
Nataly Zhukova
St. Petersburg Electrotechnical
University “LETI”
E-mail: nazhukova@mail.ru
AIST-2015, April, 9-11, Yekaterinburg
САНКТ-ПЕТЕРБУРГСКИЙ ГОСУДАРСТВЕННЫЙ ЭЛЕКТРОТЕХНИЧЕСКИЙ УНИВЕРСИТЕТ «ЛЭТИ»

Data Processing Applications Current State
SPbETU «LETI» www.eltech.ru

Outline
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4
5
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7

Recommender Systems

Problem statement
Jena

Information and Knowledge of the Subject Domain of
Measurements Processing
Providing information
and knowledge
Creation and
assessment of
information and
knowledge
Using result of data
processing
New data processing
and analyses
Retrieving new
knowledge from
historical data
Solving complicated
task of data processing
Solving complicated
specialized task
Extending available
knowledge and
information
Building, Improving and
estimating ontology
Application of
knowledge for solving
task of data processing
Exchanging information
and knowledge in
standard formats
Receiving information
and knowledge

Features of the subject domain of measurements processing
• Structured binary streams, time series or separate
measurements
Initial measurements
• Huge volume, bad quality, heterogeneity, distribution in time
and space, non stationary behavior of time series, multiple
complicated relations
Measurements features
• Formalized knowledge about measurements
Requirements to the results
• Results of data processing are used for solving tasks at the
level of objects, situations and for decision making support
Consumers

Basic agile concepts
Analyses
Design
ImplementationTesting
Evaluation

Agile Сoncepts for DPAS
DPA RS
DPAS
Design
Development
(methodological
aspect)
Development
(implementation
aspect)
Support
First level of agile
features support
(industrial level)
Second level of
agile features support
(research-oriented
level level)
Ready
methodological
solutions
Ready
technological
solutions
Information
Systems (IT
sphere)
Scientific
prototypes
Base level of agile
features support
(IT level)
Third level of
agile features support
(research level)
Ready
technological
solutions
Execution
Suggestions for
new algorithms
Ready
implementation
of algorithms
Ready
technological
solutions
Ready
technological
solutions
Life cycle Life cycle
System Agility
support

Common and Agile Features of DPA RS
AgilefeaturesandDPARStechnologiesleadtonewproblemsAgilefeaturesandDPARStechnologiesleadtonewproblems
Features are based on technologiesFeatures are based on technologies
Technologies for DPA RSTechnologies for DPA RS
Technologies for RS Technologies for DPA RS
Content-based approach
Collaborative filtering
Hybrids
Knowledge-based approach
Logical inference
Experience-based approach
Exploration analyses
DPA RS featuresDPA RS features
RS features Agile features
Capability to process huge
amounts of data
Capability to make suggestions
Ranging capabilities
Easy integration of new methods and algorithms
Easy development of new methods and algorithms
Easy extension of data processing and analyses
systems business logic
DPA RS problemsDPA RS problems
Easy integration of new
methods and algorithms
Easy development of new
methods and algorithms
Easy extension of data
processing and analyses systems
business logic
Low cost of design,
development and support
Short time of design,
development and support
Convenient working space

DPA RS Information Model
Model of the applied subject
domain of data processing
and analyses
Model of the applied subject
domain of data processing
and analyses
DPAS dynamic information model
Information model of the environmentInformation model of the environment
Subject domain modelsSubject domain models
DPAS information modelDPAS information model
RS DPAS information modelRS DPAS information model
DPAS satic information modelDPAS satic information model
Model of the applied
subject domain
Model of the applied
subject domain
DPAS RS dynamic information
model DPAS RS static information modelDPAS RS static information model
<<inherits>> <<inherits>>
<<inherits>> <<inherits>> <<inherits>>
<<uses>>
<<uses>>
<<inherits>>
<<uses>>

DPA RS Architecture
RS GUI tools GUI Processes management tool
Ontologies and knowledge
bases editors
RS tasks manager
RS content manager
Ontologies
Service of mathematical
and modeling libraries
Service for external
connections management
and support
Data, information and
knowledge visualization
service
Processes management
and execution service
Network
Nework
Knowledge bases
Data, information and
knowledge sever
Data bases File storage
Data, information and knowledge
Backend
Administrative service
Data processing and
analyses service
Data processing and analyses
tools
Frontend
Inference machine
RS ontologies
RS knowledge bases
RS data, information and
knowledge manager
DPAS componentsDPAS componentsDPA RS componentsDPA RS components
Research-oriented
services

Case study
The system has the aim to analyze and control structure and contents of the
binary streams received from space objects.
Example of the binary steams structure

General procedure for the binary streams
processing and analyses
Apply methods based on calculation of the frequency distribution of the streamsApply methods based on calculation of the frequency distribution of the streams
Binary streams
Compare the descriptions with the descriptions of the earlier received streamsCompare the descriptions with the descriptions of the earlier received streams
Preliminary streams descriptions
Similar streams are found?
Restore the length of the cards and words, subcommutation and
supercommutation of the parameters using methods of correlation analyses
Restore the length of the cards and words, subcommutation and
supercommutation of the parameters using methods of correlation analyses
The structure needs improvements?
Build and analyze the graphs that represent the structure of the streamsBuild and analyze the graphs that represent the structure of the streams
Streams descriptions

GUI of program complexes for processing
stream structure

Experimental results
Parameters of the stream
98 80 91,3 90,1
92 40 90,2 92,4
98 78 93 89,5
80 20 72,3 71,7
wL Sub SupfL
%60|| Sub
%60|| Sup
%5DP
%35SVP
%40FVP
%10FPP
%10OP
%5DP
%35SVP
%10FVP
%40FPP
%10OP

Contacts
Contact information:
Nataly Zhukova
5, Prof. Popova str, St. Petersburg, 197376
E-mail:
nazhukova@mail.ru
Phone:
+7 812 346-46-41
Fax:
+7 812 346-46-41

Alexander Vodyaho & Nataly Zhukova — Implementation of Agile Concepts in Recommender Systems for Data Processing and Analyses

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