This document describes an architecture called AgentMatcher that matches learners to learning objects. It extracts metadata from learning objects using a tool called LOMGen to generate Canadian Learning Object Metadata. It allows users to submit queries by selecting metadata terms and weights. The queries and learning object metadata are translated to a format called Weighted Object-Oriented RuleML and compared to find similar learning objects. Results above a set threshold are returned to the user ranked by similarity. LOMGen semi-automatically generates metadata terms from learning object content to assist the metadata generation process.
The AgentMatcher system matches learners and learning objects (LOs) using a tree-structured representation of metadata. It extracts metadata from LOs using LOMGen and stores it in a database. Learners can enter query parameters as a weighted tree, which is compared to LO metadata trees to find similar LOs. Top matches above a similarity threshold are returned to the learner. LOMGen semi-automatically generates metadata using keywords and allows an administrator to refine selections. This enhances precision over simple keyword searches.
The document discusses different Java platforms:
- J2SE is for desktop/workstation applications, J2ME is for consumer devices, and J2EE is for server applications.
- J2EE supports technologies like XML, web applications, web services, and defines APIs like JAXP. It uses service technologies like JDBC and communication technologies like JMS.
- The main J2EE components are EJBs, which are reusable software units containing business logic packaged as Java classes and XML files. There are session beans for single clients, entity beans for persistent data, and message-driven beans.
1. XML and Java work well together, with XML providing portable data and Java providing portable code.
2. XML can be used to represent structured data in a standard way that facilitates sharing data between systems.
3. Servlets allow Java code to dynamically extend the functionality of web servers, providing a platform-independent way to build distributed applications. Servlets can leverage web server technologies like security, scalability, and integration into websites.
1. WEKA is a popular open source machine learning software suite developed at the University of Waikato. It contains tools for data preprocessing, classification, regression, clustering, association rule mining, and visualization.
2. WEKA's main advantages include being portable, supporting a wide range of data mining tasks, having a user-friendly graphical interface, and allowing easy comparison of different machine learning techniques.
3. The WEKA Explorer provides a graphical environment for loading and preprocessing data, training and evaluating machine learning models, and visualizing results. Key functionality includes classification, clustering, attribute selection, and visualization tabs.
This document discusses working in the Oracle Forms Builder environment. It describes Forms Builder components like the Object Navigator and Layout Editor. It explains how to navigate the Forms Builder interface, identify different objects in a form module, customize the Forms Builder session, use online help, and identify main Forms executables. It also describes form module types and how to set environment variables for design and run time. It provides instructions for running a form from within Forms Builder.
The document discusses application packages and classes in PeopleSoft. It defines what an application package and class are, and explains how to create them using Application Designer. It also covers object-oriented concepts like classes, objects, encapsulation, inheritance, polymorphism. Additionally, it discusses class structure, importing packages and classes, access controls, defining methods, abstract methods, interfaces, constructors, get/set methods, and exception handling.
- Software rewrites are tempting but often more difficult than initially expected. It's better to write code that can coexist with existing software as an optional add-on.
- When using new systems or technologies, plan to build a "throwaway" version first since the best planning won't get it right the first time.
- For early-stage products, don't sink too much time and money into implementation until proving market viability. Build minimal throwaway proof-of-concept versions instead.
- Code is generally harder to read than write, so old code often seems like a "mess" even if it's not.
Problem 1 – First-Order Predicate Calculus (15 points)butest
This document contains a 10 page machine learning exam for a course at the University of Wisconsin - Madison. The exam consists of 6 problems testing knowledge of topics like Naive Bayes classification, decision trees, neural networks, reinforcement learning, inductive logic programming, and more. It provides training examples, diagrams and asks students to show calculations, describe algorithms, and discuss key concepts in machine learning.
The AgentMatcher system matches learners and learning objects (LOs) using a tree-structured representation of metadata. It extracts metadata from LOs using LOMGen and stores it in a database. Learners can enter query parameters as a weighted tree, which is compared to LO metadata trees to find similar LOs. Top matches above a similarity threshold are returned to the learner. LOMGen semi-automatically generates metadata using keywords and allows an administrator to refine selections. This enhances precision over simple keyword searches.
The document discusses different Java platforms:
- J2SE is for desktop/workstation applications, J2ME is for consumer devices, and J2EE is for server applications.
- J2EE supports technologies like XML, web applications, web services, and defines APIs like JAXP. It uses service technologies like JDBC and communication technologies like JMS.
- The main J2EE components are EJBs, which are reusable software units containing business logic packaged as Java classes and XML files. There are session beans for single clients, entity beans for persistent data, and message-driven beans.
1. XML and Java work well together, with XML providing portable data and Java providing portable code.
2. XML can be used to represent structured data in a standard way that facilitates sharing data between systems.
3. Servlets allow Java code to dynamically extend the functionality of web servers, providing a platform-independent way to build distributed applications. Servlets can leverage web server technologies like security, scalability, and integration into websites.
1. WEKA is a popular open source machine learning software suite developed at the University of Waikato. It contains tools for data preprocessing, classification, regression, clustering, association rule mining, and visualization.
2. WEKA's main advantages include being portable, supporting a wide range of data mining tasks, having a user-friendly graphical interface, and allowing easy comparison of different machine learning techniques.
3. The WEKA Explorer provides a graphical environment for loading and preprocessing data, training and evaluating machine learning models, and visualizing results. Key functionality includes classification, clustering, attribute selection, and visualization tabs.
This document discusses working in the Oracle Forms Builder environment. It describes Forms Builder components like the Object Navigator and Layout Editor. It explains how to navigate the Forms Builder interface, identify different objects in a form module, customize the Forms Builder session, use online help, and identify main Forms executables. It also describes form module types and how to set environment variables for design and run time. It provides instructions for running a form from within Forms Builder.
The document discusses application packages and classes in PeopleSoft. It defines what an application package and class are, and explains how to create them using Application Designer. It also covers object-oriented concepts like classes, objects, encapsulation, inheritance, polymorphism. Additionally, it discusses class structure, importing packages and classes, access controls, defining methods, abstract methods, interfaces, constructors, get/set methods, and exception handling.
- Software rewrites are tempting but often more difficult than initially expected. It's better to write code that can coexist with existing software as an optional add-on.
- When using new systems or technologies, plan to build a "throwaway" version first since the best planning won't get it right the first time.
- For early-stage products, don't sink too much time and money into implementation until proving market viability. Build minimal throwaway proof-of-concept versions instead.
- Code is generally harder to read than write, so old code often seems like a "mess" even if it's not.
Problem 1 – First-Order Predicate Calculus (15 points)butest
This document contains a 10 page machine learning exam for a course at the University of Wisconsin - Madison. The exam consists of 6 problems testing knowledge of topics like Naive Bayes classification, decision trees, neural networks, reinforcement learning, inductive logic programming, and more. It provides training examples, diagrams and asks students to show calculations, describe algorithms, and discuss key concepts in machine learning.
Paragon Software announces the release of Paragon NTFS for Mac OS X 8.0, which provides full read and write access to NTFS partitions on Macs. It is the fastest NTFS driver on the market, achieving speeds comparable to native Mac file systems. Paragon NTFS for Mac 8.0 fully supports the latest Mac OS X Snow Leopard operating system in 64-bit mode and allows easy transfer of files between Windows and Mac partitions without additional hardware or software.
This document discusses classifying brain MRI series using decision tree learning. It proposes a two-level classification method: 1) classifying segmented MRI images into low-level features like size and texture, and 2) classifying entire MRI series into conditions (normal, infarction, tumor) using synthesized high-level features. Decision trees are used at both levels to achieve high accuracy. Experiments were conducted to classify brain MRI series into three common conditions.
Prof. A. Taleb-Bendiab presented research on a machine learning middleware service for autonomic computing. The service uses machine learning techniques like self-organizing maps for user classification and on-demand reservation of grid services. Two experiments were conducted: one classified users based on connected home device usage patterns, while another reserved applications services on demand. Further work involves integrating the service with the Neptune meta-language to support norm-governed web services and architectures, and using machine learning for danger/novelty detection in autonomic systems.
The document is a grant application for schools participating in the Fuel Up to Play 60 program. It provides instructions for applying for a minimum $350 grant, including eligibility criteria such as forming a student team and selecting a healthy eating and physical activity program. It requests information on the school, planned activities, and how grant funds would be used to support the Fuel Up to Play 60 kickoff event and programs. The deadline to submit the application is February 12, 2010.
Accurately and Reliably Extracting Data from the Web: butest
STALKER is a machine learning algorithm that learns to extract data from web pages using a small number of labeled examples provided by the user. It generates extraction rules in a hierarchical manner, exploiting the structure of the web source. The algorithm is efficient because most web pages have a fixed template with few variations. It also uses an active learning approach called co-testing to select the most informative examples for the user to label. The system verifies extracted data by comparing it to learned statistical patterns, and can automatically repair wrappers when sites change.
Sponsored Search Acution Design Via Machine Learningbutest
This document discusses using machine learning techniques for mechanism design and pricing problems in economics. Specifically, it explores using a random sampling auction where bidders are split into two groups, an optimization algorithm is run on one group, and the prices from that are applied to the other group. The goal is to show that as the number of bidders or optimal profit increases relative to the number of possible pricing functions, the random sampling auction performs close to the best fixed pricing function. Several challenges are discussed, such as how to define what needs to be large and how to incorporate regularization.
This document evaluates several supervised machine learning algorithms for classifying gene expression data from microarray experiments. It describes analyzing two gene expression datasets, the leukemia and DLBCL datasets, using k-nearest neighbors, naive Bayes, decision trees, and support vector machines with and without feature selection. The results show that support vector machines achieved the best performance overall, and that feature selection improved the accuracy of all the algorithms.
Creation of a Test Bed Environment for Core Java Applications using White Box...cscpconf
A Test Bed Environment allows for rigorous, transparent, and replicable testing of scientific
theories. However, in software development these test beds can be specified hardware and
software environment for the application under test. Though the existing open source test bed
environments in Integrated Development Environment (IDE)s are capable of supporting the
development of Java application types, test reports are generated by third party developers.
They do not enhance the utility and the performance of the system constructed. Our proposed
system, we have created a customized test bed environment for core java application programs
used to generate the test case report using generated control flow graph. This can be obtained by developing a new mini compiler with additional features.
An Efficient Annotation of Search Results Based on Feature Ranking Approach f...Computer Science Journals
With the increased number of web databases, major part of deep web is one of the bases of database. In several search engines, encoded data in the returned resultant pages from the web often comes from structured databases which are referred as Web databases (WDB).
Aloa - A Web Services Driven Framework for Automatic Learning Objcet AnnotationMohamed Amine Chatti
ALOA is a framework for automatically generating metadata for learning objects (LOs). It uses a service-oriented architecture and web services to allow for flexible and extensible metadata generation. ALOA's core engine indexes LOs, extracts properties from them using extractors, generates metadata using generators, resolves conflicts, and translates the metadata. New extractors and generators can be easily plugged into ALOA. It provides automatically generated LOM metadata for online LOs in different formats and languages through a web services API.
final Year Projects, Final Year Projects in Chennai, Software Projects, Embedded Projects, Microcontrollers Projects, DSP Projects, VLSI Projects, Matlab Projects, Java Projects, .NET Projects, IEEE Projects, IEEE 2009 Projects, IEEE 2009 Projects, Software, IEEE 2009 Projects, Embedded, Software IEEE 2009 Projects, Embedded IEEE 2009 Projects, Final Year Project Titles, Final Year Project Reports, Final Year Project Review, Robotics Projects, Mechanical Projects, Electrical Projects, Power Electronics Projects, Power System Projects, Model Projects, Java Projects, J2EE Projects, Engineering Projects, Student Projects, Engineering College Projects, MCA Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, Wireless Networks Projects, Network Security Projects, Networking Projects, final year projects, ieee projects, student projects, college projects, ieee projects in chennai, java projects, software ieee projects, embedded ieee projects, "ieee2009projects", "final year projects", "ieee projects", "Engineering Projects", "Final Year Projects in Chennai", "Final year Projects at Chennai", Java Projects, ASP.NET Projects, VB.NET Projects, C# Projects, Visual C++ Projects, Matlab Projects, NS2 Projects, C Projects, Microcontroller Projects, ATMEL Projects, PIC Projects, ARM Projects, DSP Projects, VLSI Projects, FPGA Projects, CPLD Projects, Power Electronics Projects, Electrical Projects, Robotics Projects, Solor Projects, MEMS Projects, J2EE Projects, J2ME Projects, AJAX Projects, Structs Projects, EJB Projects, Real Time Projects, Live Projects, Student Projects, Engineering Projects, MCA Projects, MBA Projects, College Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, M.Sc Projects, Final Year Java Projects, Final Year ASP.NET Projects, Final Year VB.NET Projects, Final Year C# Projects, Final Year Visual C++ Projects, Final Year Matlab Projects, Final Year NS2 Projects, Final Year C Projects, Final Year Microcontroller Projects, Final Year ATMEL Projects, Final Year PIC Projects, Final Year ARM Projects, Final Year DSP Projects, Final Year VLSI Projects, Final Year FPGA Projects, Final Year CPLD Projects, Final Year Power Electronics Projects, Final Year Electrical Projects, Final Year Robotics Projects, Final Year Solor Projects, Final Year MEMS Projects, Final Year J2EE Projects, Final Year J2ME Projects, Final Year AJAX Projects, Final Year Structs Projects, Final Year EJB Projects, Final Year Real Time Projects, Final Year Live Projects, Final Year Student Projects, Final Year Engineering Projects, Final Year MCA Projects, Final Year MBA Projects, Final Year College Projects, Final Year BE Projects, Final Year BTech Projects, Final Year ME Projects, Final Year MTech Projects, Final Year M.Sc Projects, IEEE Java Projects, ASP.NET Projects, VB.NET Projects, C# Projects, Visual C++ Projects, Matlab Projects, NS2 Projects, C Projects, Microcontroller Projects, ATMEL Projects, PIC Projects, ARM Projects, DSP Projects, VLSI Projects, FPGA Projects, CPLD Projects, Power Electronics Projects, Electrical Projects, Robotics Projects, Solor Projects, MEMS Projects, J2EE Projects, J2ME Projects, AJAX Projects, Structs Projects, EJB Projects, Real Time Projects, Live Projects, Student Projects, Engineering Projects, MCA Projects, MBA Projects, College Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, M.Sc Projects, IEEE 2009 Java Projects, IEEE 2009 ASP.NET Projects, IEEE 2009 VB.NET Projects, IEEE 2009 C# Projects, IEEE 2009 Visual C++ Projects, IEEE 2009 Matlab Projects, IEEE 2009 NS2 Projects, IEEE 2009 C Projects, IEEE 2009 Microcontroller Projects, IEEE 2009 ATMEL Projects, IEEE 2009 PIC Projects, IEEE 2009 ARM Projects, IEEE 2009 DSP Projects, IEEE 2009 VLSI Projects, IEEE 2009 FPGA Projects, IEEE 2009 CPLD Projects, IEEE 2009 Power Electronics Projects, IEEE 2009 Electrical Projects, IEEE 2009 Robotics Projects, IEEE 2009 Solor Projects, IEEE 2009 MEMS Projects, IEEE 2009 J2EE P
The document describes an Automatic Database Schema Generator tool that can generate a database schema from natural language textual requirements. It takes textual requirements as input, analyzes the text using natural language processing techniques like tokenization and part-of-speech tagging. It also parses a domain ontology related to the problem domain to help identify entities and attributes. The tool then extracts entities, attributes, and identifies primary and foreign keys to generate a relational database schema that can be used to develop the application database. The tool aims to automate the manual and iterative process of database schema design from requirements.
Annotation for query result records based on domain specific ontologyijnlc
The World Wide Web is enriched with a large collection of data, scattered in deep web databases and web
pages in unstructured or semi structured formats. Recently evolving customer friendly web applications
need special data extraction mechanisms to draw out the required data from these deep web, according to
the end user query and populate to the output page dynamically at the fastest rate. In existing research
areas web data extraction methods are based on the supervised learning (wrapper induction) methods. In
the past few years researchers depicted on the automatic web data extraction methods based on similarity
measures. Among automatic data extraction methods our existing Combining Tag and Value similarity
method, lags to identify an attribute in the query result table. A novel approach for data extracting and
label assignment called Annotation for Query Result Records based on domain specific ontology. First, an
ontology domain is to be constructed using information from query interface and query result pages
obtained from the web. Next, using this domain ontology, a meaning label is assigned automatically to each
column of the extracted query result records.
AUTOMATIC CONVERSION OF RELATIONAL DATABASES INTO ONTOLOGIES: A COMPARATIVE A...IJwest
Constructing ontologies from relational databases is an active research topic in the Semantic Web domain.
While conceptual mapping rules/principles of relational databases and ontology structures are being
proposed, several software modules or plug-ins are being developed to enable the automatic conversion of
relational databases into ontologies. However, the correlation between the resulting ontologies built
automatically with plug-ins from relational databases and the database-toontology mapping principles has
been given little attention. This study reviews and applies two Protégé plug-ins, namely, DataMaster and
OntoBase to automatically construct ontologies from a relational database. The resulting ontologies are
further analysed to match their structures against the database-to-ontology mapping principles. A
comparative analysis of the matching results reveals that OntoBase outperforms DataMaster in applying
the database-to-ontology mapping principles for automatically converting relational databases into
ontologies
Vision Based Deep Web data Extraction on Nested Query Result RecordsIJMER
This document summarizes a research paper on vision-based deep web data extraction from nested query result records. It proposes a technique to extract data from web pages using different font styles, sizes, and cascading style sheets. The extracted data is then aligned into a table using alignment algorithms, including pair-wise, holistic, and nested-structure alignment. The goal is to remove immaterial information from query result pages to facilitate analysis of the extracted data.
Here are the key steps to configure Spring MVC in the lab:
1. Configure the ContextLoaderListener in web.xml to initialize the root application context. This loads the common beans.
2. Define the contextConfigLocation parameter pointing to the common spring configuration files.
3. Configure the DispatcherServlet in web.xml. This is the front controller that handles all web requests.
4. Give the DispatcherServlet a unique name and set its contextConfigLocation to load web-specific beans, separate from the root context.
5. Add spring-mvc configuration files defining the component-scan, view resolver, etc.
6. Add Controllers and Views (JSP
Generating requirements analysis models from textual requiremenfortes
This document describes a process for generating use case models from textual requirements. The process uses the EA-Miner tool to analyze textual requirements and extract information like functional concerns, RDL sentences, and a syntactically tagged document. This extracted information is used to derive initial candidate use cases, actors, and relationships. The candidate model is then refined by activities like removing undesirable use cases, completing abstraction names, adding new use cases/actors, and defining relationships between use cases. The overall goal is to reduce the time and effort required to produce requirements artifacts from textual specifications.
11.query optimization to improve performance of the code executionAlexander Decker
1. The document discusses query optimization techniques to improve the performance of object querying in Java.
2. It presents the Java Query Language (JQL) which allows programmers to express queries over object collections in Java through a declarative syntax.
3. The key aspects of JQL implementation include a compiler that compiles JQL queries to Java code and a query evaluator that applies optimizations like hash joins and nested loops joins to efficiently evaluate the queries.
Query optimization to improve performance of the code executionAlexander Decker
This document discusses query optimization techniques to improve the performance of code execution. It describes how object querying provides an abstraction for operations over collections of objects that allows the query evaluator to optimize queries dynamically at runtime. Specifically, it presents an example of using the Java Query Language (JQL) to perform an equi-join on two collections in a more succinct way compared to manually iterating over the collections, and discusses how the JQL query could be optimized using techniques like hash joins.
ALOA: A Web Services Driven Framework for Automatic Learning Object AnnotationMohamed Amine Chatti
ALOA is a framework for automatically generating metadata for learning objects (LOs). It extracts information from LOs using pluggable extractors and generates metadata using pluggable generators. ALOA is flexible and extensible through its use of a service-oriented architecture and web services. It can generate LOM metadata from different file types like HTML, PDF and generate metadata in multiple languages.
Semi Automatic to Improve Ontology Mapping Process in Semantic Web Data AnalysisIRJET Journal
This document summarizes a research paper about developing a semi-automatic ontology mapping system to improve integration of data from different ontologies on the semantic web. It discusses how the system uses techniques from computational linguistics, information retrieval, and machine learning to map ontologies in an iterative process. The system performs various natural language processing tasks and leverages external resources like domain thesauri and WordNet to strengthen matches during the mapping process. Preliminary case studies show promising results for the semi-automatic ontology mapping system.
Paragon Software announces the release of Paragon NTFS for Mac OS X 8.0, which provides full read and write access to NTFS partitions on Macs. It is the fastest NTFS driver on the market, achieving speeds comparable to native Mac file systems. Paragon NTFS for Mac 8.0 fully supports the latest Mac OS X Snow Leopard operating system in 64-bit mode and allows easy transfer of files between Windows and Mac partitions without additional hardware or software.
This document discusses classifying brain MRI series using decision tree learning. It proposes a two-level classification method: 1) classifying segmented MRI images into low-level features like size and texture, and 2) classifying entire MRI series into conditions (normal, infarction, tumor) using synthesized high-level features. Decision trees are used at both levels to achieve high accuracy. Experiments were conducted to classify brain MRI series into three common conditions.
Prof. A. Taleb-Bendiab presented research on a machine learning middleware service for autonomic computing. The service uses machine learning techniques like self-organizing maps for user classification and on-demand reservation of grid services. Two experiments were conducted: one classified users based on connected home device usage patterns, while another reserved applications services on demand. Further work involves integrating the service with the Neptune meta-language to support norm-governed web services and architectures, and using machine learning for danger/novelty detection in autonomic systems.
The document is a grant application for schools participating in the Fuel Up to Play 60 program. It provides instructions for applying for a minimum $350 grant, including eligibility criteria such as forming a student team and selecting a healthy eating and physical activity program. It requests information on the school, planned activities, and how grant funds would be used to support the Fuel Up to Play 60 kickoff event and programs. The deadline to submit the application is February 12, 2010.
Accurately and Reliably Extracting Data from the Web: butest
STALKER is a machine learning algorithm that learns to extract data from web pages using a small number of labeled examples provided by the user. It generates extraction rules in a hierarchical manner, exploiting the structure of the web source. The algorithm is efficient because most web pages have a fixed template with few variations. It also uses an active learning approach called co-testing to select the most informative examples for the user to label. The system verifies extracted data by comparing it to learned statistical patterns, and can automatically repair wrappers when sites change.
Sponsored Search Acution Design Via Machine Learningbutest
This document discusses using machine learning techniques for mechanism design and pricing problems in economics. Specifically, it explores using a random sampling auction where bidders are split into two groups, an optimization algorithm is run on one group, and the prices from that are applied to the other group. The goal is to show that as the number of bidders or optimal profit increases relative to the number of possible pricing functions, the random sampling auction performs close to the best fixed pricing function. Several challenges are discussed, such as how to define what needs to be large and how to incorporate regularization.
This document evaluates several supervised machine learning algorithms for classifying gene expression data from microarray experiments. It describes analyzing two gene expression datasets, the leukemia and DLBCL datasets, using k-nearest neighbors, naive Bayes, decision trees, and support vector machines with and without feature selection. The results show that support vector machines achieved the best performance overall, and that feature selection improved the accuracy of all the algorithms.
Creation of a Test Bed Environment for Core Java Applications using White Box...cscpconf
A Test Bed Environment allows for rigorous, transparent, and replicable testing of scientific
theories. However, in software development these test beds can be specified hardware and
software environment for the application under test. Though the existing open source test bed
environments in Integrated Development Environment (IDE)s are capable of supporting the
development of Java application types, test reports are generated by third party developers.
They do not enhance the utility and the performance of the system constructed. Our proposed
system, we have created a customized test bed environment for core java application programs
used to generate the test case report using generated control flow graph. This can be obtained by developing a new mini compiler with additional features.
An Efficient Annotation of Search Results Based on Feature Ranking Approach f...Computer Science Journals
With the increased number of web databases, major part of deep web is one of the bases of database. In several search engines, encoded data in the returned resultant pages from the web often comes from structured databases which are referred as Web databases (WDB).
Aloa - A Web Services Driven Framework for Automatic Learning Objcet AnnotationMohamed Amine Chatti
ALOA is a framework for automatically generating metadata for learning objects (LOs). It uses a service-oriented architecture and web services to allow for flexible and extensible metadata generation. ALOA's core engine indexes LOs, extracts properties from them using extractors, generates metadata using generators, resolves conflicts, and translates the metadata. New extractors and generators can be easily plugged into ALOA. It provides automatically generated LOM metadata for online LOs in different formats and languages through a web services API.
final Year Projects, Final Year Projects in Chennai, Software Projects, Embedded Projects, Microcontrollers Projects, DSP Projects, VLSI Projects, Matlab Projects, Java Projects, .NET Projects, IEEE Projects, IEEE 2009 Projects, IEEE 2009 Projects, Software, IEEE 2009 Projects, Embedded, Software IEEE 2009 Projects, Embedded IEEE 2009 Projects, Final Year Project Titles, Final Year Project Reports, Final Year Project Review, Robotics Projects, Mechanical Projects, Electrical Projects, Power Electronics Projects, Power System Projects, Model Projects, Java Projects, J2EE Projects, Engineering Projects, Student Projects, Engineering College Projects, MCA Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, Wireless Networks Projects, Network Security Projects, Networking Projects, final year projects, ieee projects, student projects, college projects, ieee projects in chennai, java projects, software ieee projects, embedded ieee projects, "ieee2009projects", "final year projects", "ieee projects", "Engineering Projects", "Final Year Projects in Chennai", "Final year Projects at Chennai", Java Projects, ASP.NET Projects, VB.NET Projects, C# Projects, Visual C++ Projects, Matlab Projects, NS2 Projects, C Projects, Microcontroller Projects, ATMEL Projects, PIC Projects, ARM Projects, DSP Projects, VLSI Projects, FPGA Projects, CPLD Projects, Power Electronics Projects, Electrical Projects, Robotics Projects, Solor Projects, MEMS Projects, J2EE Projects, J2ME Projects, AJAX Projects, Structs Projects, EJB Projects, Real Time Projects, Live Projects, Student Projects, Engineering Projects, MCA Projects, MBA Projects, College Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, M.Sc Projects, Final Year Java Projects, Final Year ASP.NET Projects, Final Year VB.NET Projects, Final Year C# Projects, Final Year Visual C++ Projects, Final Year Matlab Projects, Final Year NS2 Projects, Final Year C Projects, Final Year Microcontroller Projects, Final Year ATMEL Projects, Final Year PIC Projects, Final Year ARM Projects, Final Year DSP Projects, Final Year VLSI Projects, Final Year FPGA Projects, Final Year CPLD Projects, Final Year Power Electronics Projects, Final Year Electrical Projects, Final Year Robotics Projects, Final Year Solor Projects, Final Year MEMS Projects, Final Year J2EE Projects, Final Year J2ME Projects, Final Year AJAX Projects, Final Year Structs Projects, Final Year EJB Projects, Final Year Real Time Projects, Final Year Live Projects, Final Year Student Projects, Final Year Engineering Projects, Final Year MCA Projects, Final Year MBA Projects, Final Year College Projects, Final Year BE Projects, Final Year BTech Projects, Final Year ME Projects, Final Year MTech Projects, Final Year M.Sc Projects, IEEE Java Projects, ASP.NET Projects, VB.NET Projects, C# Projects, Visual C++ Projects, Matlab Projects, NS2 Projects, C Projects, Microcontroller Projects, ATMEL Projects, PIC Projects, ARM Projects, DSP Projects, VLSI Projects, FPGA Projects, CPLD Projects, Power Electronics Projects, Electrical Projects, Robotics Projects, Solor Projects, MEMS Projects, J2EE Projects, J2ME Projects, AJAX Projects, Structs Projects, EJB Projects, Real Time Projects, Live Projects, Student Projects, Engineering Projects, MCA Projects, MBA Projects, College Projects, BE Projects, BTech Projects, ME Projects, MTech Projects, M.Sc Projects, IEEE 2009 Java Projects, IEEE 2009 ASP.NET Projects, IEEE 2009 VB.NET Projects, IEEE 2009 C# Projects, IEEE 2009 Visual C++ Projects, IEEE 2009 Matlab Projects, IEEE 2009 NS2 Projects, IEEE 2009 C Projects, IEEE 2009 Microcontroller Projects, IEEE 2009 ATMEL Projects, IEEE 2009 PIC Projects, IEEE 2009 ARM Projects, IEEE 2009 DSP Projects, IEEE 2009 VLSI Projects, IEEE 2009 FPGA Projects, IEEE 2009 CPLD Projects, IEEE 2009 Power Electronics Projects, IEEE 2009 Electrical Projects, IEEE 2009 Robotics Projects, IEEE 2009 Solor Projects, IEEE 2009 MEMS Projects, IEEE 2009 J2EE P
The document describes an Automatic Database Schema Generator tool that can generate a database schema from natural language textual requirements. It takes textual requirements as input, analyzes the text using natural language processing techniques like tokenization and part-of-speech tagging. It also parses a domain ontology related to the problem domain to help identify entities and attributes. The tool then extracts entities, attributes, and identifies primary and foreign keys to generate a relational database schema that can be used to develop the application database. The tool aims to automate the manual and iterative process of database schema design from requirements.
Annotation for query result records based on domain specific ontologyijnlc
The World Wide Web is enriched with a large collection of data, scattered in deep web databases and web
pages in unstructured or semi structured formats. Recently evolving customer friendly web applications
need special data extraction mechanisms to draw out the required data from these deep web, according to
the end user query and populate to the output page dynamically at the fastest rate. In existing research
areas web data extraction methods are based on the supervised learning (wrapper induction) methods. In
the past few years researchers depicted on the automatic web data extraction methods based on similarity
measures. Among automatic data extraction methods our existing Combining Tag and Value similarity
method, lags to identify an attribute in the query result table. A novel approach for data extracting and
label assignment called Annotation for Query Result Records based on domain specific ontology. First, an
ontology domain is to be constructed using information from query interface and query result pages
obtained from the web. Next, using this domain ontology, a meaning label is assigned automatically to each
column of the extracted query result records.
AUTOMATIC CONVERSION OF RELATIONAL DATABASES INTO ONTOLOGIES: A COMPARATIVE A...IJwest
Constructing ontologies from relational databases is an active research topic in the Semantic Web domain.
While conceptual mapping rules/principles of relational databases and ontology structures are being
proposed, several software modules or plug-ins are being developed to enable the automatic conversion of
relational databases into ontologies. However, the correlation between the resulting ontologies built
automatically with plug-ins from relational databases and the database-toontology mapping principles has
been given little attention. This study reviews and applies two Protégé plug-ins, namely, DataMaster and
OntoBase to automatically construct ontologies from a relational database. The resulting ontologies are
further analysed to match their structures against the database-to-ontology mapping principles. A
comparative analysis of the matching results reveals that OntoBase outperforms DataMaster in applying
the database-to-ontology mapping principles for automatically converting relational databases into
ontologies
Vision Based Deep Web data Extraction on Nested Query Result RecordsIJMER
This document summarizes a research paper on vision-based deep web data extraction from nested query result records. It proposes a technique to extract data from web pages using different font styles, sizes, and cascading style sheets. The extracted data is then aligned into a table using alignment algorithms, including pair-wise, holistic, and nested-structure alignment. The goal is to remove immaterial information from query result pages to facilitate analysis of the extracted data.
Here are the key steps to configure Spring MVC in the lab:
1. Configure the ContextLoaderListener in web.xml to initialize the root application context. This loads the common beans.
2. Define the contextConfigLocation parameter pointing to the common spring configuration files.
3. Configure the DispatcherServlet in web.xml. This is the front controller that handles all web requests.
4. Give the DispatcherServlet a unique name and set its contextConfigLocation to load web-specific beans, separate from the root context.
5. Add spring-mvc configuration files defining the component-scan, view resolver, etc.
6. Add Controllers and Views (JSP
Generating requirements analysis models from textual requiremenfortes
This document describes a process for generating use case models from textual requirements. The process uses the EA-Miner tool to analyze textual requirements and extract information like functional concerns, RDL sentences, and a syntactically tagged document. This extracted information is used to derive initial candidate use cases, actors, and relationships. The candidate model is then refined by activities like removing undesirable use cases, completing abstraction names, adding new use cases/actors, and defining relationships between use cases. The overall goal is to reduce the time and effort required to produce requirements artifacts from textual specifications.
11.query optimization to improve performance of the code executionAlexander Decker
1. The document discusses query optimization techniques to improve the performance of object querying in Java.
2. It presents the Java Query Language (JQL) which allows programmers to express queries over object collections in Java through a declarative syntax.
3. The key aspects of JQL implementation include a compiler that compiles JQL queries to Java code and a query evaluator that applies optimizations like hash joins and nested loops joins to efficiently evaluate the queries.
Query optimization to improve performance of the code executionAlexander Decker
This document discusses query optimization techniques to improve the performance of code execution. It describes how object querying provides an abstraction for operations over collections of objects that allows the query evaluator to optimize queries dynamically at runtime. Specifically, it presents an example of using the Java Query Language (JQL) to perform an equi-join on two collections in a more succinct way compared to manually iterating over the collections, and discusses how the JQL query could be optimized using techniques like hash joins.
ALOA: A Web Services Driven Framework for Automatic Learning Object AnnotationMohamed Amine Chatti
ALOA is a framework for automatically generating metadata for learning objects (LOs). It extracts information from LOs using pluggable extractors and generates metadata using pluggable generators. ALOA is flexible and extensible through its use of a service-oriented architecture and web services. It can generate LOM metadata from different file types like HTML, PDF and generate metadata in multiple languages.
Semi Automatic to Improve Ontology Mapping Process in Semantic Web Data AnalysisIRJET Journal
This document summarizes a research paper about developing a semi-automatic ontology mapping system to improve integration of data from different ontologies on the semantic web. It discusses how the system uses techniques from computational linguistics, information retrieval, and machine learning to map ontologies in an iterative process. The system performs various natural language processing tasks and leverages external resources like domain thesauri and WordNet to strengthen matches during the mapping process. Preliminary case studies show promising results for the semi-automatic ontology mapping system.
The document discusses automatic data unit annotation in search results. It proposes a method that clusters data units on result pages into groups containing semantically similar units. Then, multiple annotators are used to predict annotation labels for each group based on features of the units. An annotation wrapper is constructed for each website to annotate new result pages from that site. The method aims to improve search response by providing meaningful annotations of data units within results. It is evaluated based on precision and recall for the alignment of data units and text nodes during the annotation process.
The intern worked at IBM's EBU department helping develop a web application project for a customer called PATAC Shanghai. Their responsibilities included coding Java interfaces for the server side using the Spring framework and MyBatis, and helping develop Angular pages for the data management system. They gained experience with Spring, MyBatis, and Angular JS frameworks and improved both technical and soft skills through workshops and group tasks during the internship.
A Novel Data Extraction and Alignment Method for Web DatabasesIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
This document discusses the process of lexical analysis in compiling or interpreting a program. It begins with an abstract discussing how lexical analysis involves turning a string of letters into tokens like keywords, identifiers, constants, and operators. It then provides background on lexical analysis, explaining that it reads input characters one by one and groups them into tokens that are passed to a parser. Key techniques for lexical analysis mentioned include using regular expressions and finite automata to identify tokens. The document also reviews related work on parallelizing lexical analysis and includes diagrams of the lexical analysis process and sample output tokens. It concludes by discussing limitations and opportunities for future work improving lexical analysis.
This document presents a study on using a custom-built Apriori algorithm for web mining and discovering frequent patterns in web log data. The key steps are: (1) preprocessing a 70MB web log file, (2) developing a custom Apriori algorithm that prunes candidate itemsets of size >2 and only considers transactions of size >=k, (3) identifying frequent patterns using the custom algorithm, (4) analyzing the discovered patterns, and (5) developing a software tool to implement the custom algorithm. Experiments show the custom algorithm takes less time than the classical Apriori algorithm. The study aims to efficiently mine useful knowledge from web data.
International Journal of Computational Engineering Research(IJCER) ijceronline
nternational Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Este documento analiza el modelo de negocio de YouTube. Explica que YouTube y otros sitios de video online representan un nuevo modelo de negocio para contenidos audiovisuales debido al cambio en los hábitos de consumo causado por las nuevas tecnologías. Describe cómo YouTube aprovecha la participación de los usuarios para mejorar continuamente y atraer una audiencia diferente a la de los medios tradicionales.
The defense was successful in portraying Michael Jackson favorably to the jury in several ways:
1) They dressed Jackson in ornate costumes that conveyed images of purity, innocence, and humility.
2) Jackson was shown entering the courtroom as if on a red carpet, emphasizing his celebrity status.
3) Jackson appeared vulnerable, childlike, and in declining health during the trial, eliciting sympathy from jurors.
4) Defense attorney Tom Mesereau effectively presented a coherent narrative of Jackson as a victim and portrayed Neverland as a place of refuge, undermining the prosecution's arguments.
Michael Jackson was born in 1958 in Gary, Indiana and rose to fame in the 1960s as the lead singer of The Jackson 5, topping music charts in the 1970s. As a solo artist in the 1980s, his album Thriller broke music records. In the 1990s and 2000s, Jackson faced several legal issues related to child abuse allegations while continuing to release music. He married Lisa Marie Presley and Debbie Rowe and had two children before his death in 2009.
Popular Reading Last Updated April 1, 2010 Adams, Lorraine The ...butest
This document appears to be a list of popular books from various authors. It includes over 150 book titles across many genres such as fiction, non-fiction, memoirs, and novels. The books cover a wide range of topics from politics to cooking to autobiographies.
The prosecution lost the Michael Jackson trial due to several key mistakes and weaknesses in their case:
1) The lead prosecutor, Thomas Sneddon, was too personally invested in the case against Jackson, having pursued him for over a decade without success.
2) Sneddon's opening statement was disorganized and weak, failing to effectively outline the prosecution's case.
3) The accuser's mother was not credible and damaged the prosecution's case through her erratic testimony, history of lies and con artist behavior.
4) Many prosecution witnesses were not credible due to prior lawsuits against Jackson, debts owed to him, or having been fired by him. Several witnesses even took the Fifth Amendment.
Here are three examples of public relations from around the world:
1. The UK government's "Be Clear on Cancer" campaign which aims to raise awareness of cancer symptoms and encourage early diagnosis.
2. Samsung's global brand marketing and sponsorship activities which aim to increase brand awareness and favorability of Samsung products worldwide.
3. The Brazilian government's efforts to improve its international image and relations with other countries through strategic communication and diplomacy.
The three most important functions of public relations are:
1. Media relations because the media is how most organizations reach their key audiences. Strong media relationships are crucial.
2. Writing, because written communication is at the core of public relations and how most information is
Michael Jackson Please Wait... provides biographical information about Michael Jackson including his birthdate, birthplace, parents, height, interests, idols, favorite foods, films, and more. It discusses his background, career highlights including influential albums like Thriller, and films he appeared in such as The Wiz and Moonwalker. The document contains photos and details about Jackson's life and illustrious music career.
The MYnstrel Free Press Volume 2: Economic Struggles, Meet Jazzbutest
The document discusses the process of manufacturing celebrity and its negative byproducts. It argues that celebrities are rarely the best in their individual pursuits like singing, dancing, etc. but become famous due to being products of a system controlled by wealthy elites. This system stifles opportunities for worthy artists and creates feudalism. The document also asserts that manufactured celebrities should not be viewed as role models due to behaviors like drug abuse and narcissism that result from the celebrity-making process.
Michael Jackson was a child star who rose to fame with the Jackson 5 in the late 1960s and early 1970s. As a solo artist in the 1970s and 1980s, he had immense commercial success with albums like Off the Wall, Thriller, and Bad, which featured hit singles and groundbreaking music videos. However, his career and public image were plagued by controversies related to allegations of child sexual abuse in the 1990s and 2000s. He continued recording and performing but faced ongoing media scrutiny into his private life until his death in 2009.
Social Networks: Twitter Facebook SL - Slide 1butest
The document discusses using social networking tools like Twitter and Facebook in K-12 education. Twitter allows students and teachers to share short updates and can be used to give parents a window into classroom activities. Facebook allows targeted advertising that could be used to promote educational activities. Both tools could help facilitate communication between schools and communities if used properly while managing privacy and security concerns.
Facebook has over 300 million active users who log on daily, and allows brands to create public profile pages to interact with users. Pages are for brands and organizations only, while groups can be made by any user about any topic. Pages do not show admin names and have no limits on fans, while groups display admin names and are limited to 5,000 members. Content on pages should aim to provoke action from subscribers and establish a regular posting schedule using a conversational tone.
Executive Summary Hare Chevrolet is a General Motors dealership ...butest
Hare Chevrolet is a car dealership located in Noblesville, Indiana that has successfully used social media platforms like Twitter, Facebook, and YouTube to create a positive brand image. They invest significant time interacting directly with customers online to foster a sense of community rather than overtly advertising. As a result, Hare Chevrolet has built a large, engaged audience on social media and serves as a model for how brands can use online presences strategically.
Welcome to the Dougherty County Public Library's Facebook and ...butest
This document provides instructions for signing up for Facebook and Twitter accounts. It outlines the sign up process for both platforms, including filling out forms with name, email, password and other details. It describes how the platforms will then search for friends and suggest people to connect with. It also explains how to search for and follow the Dougherty County Public Library page on both Facebook and Twitter once signed up. The document concludes by thanking participants and providing a contact for any additional questions.
This document provides compatibility information for Olympus digital products used with Macintosh OS X. It lists various digital cameras, photo printers, voice recorders, and accessories along with their connection type and any notes on compatibility. Some products require booting into OS 9.1 for software compatibility or do not support devices that need a serial port. Drivers and software are available for download from Olympus and other websites for many products to enable use with OS X.
To use printers managed by the university's Information Technology Services (ITS), students and faculty must install the ITS Remote Printing software on their Mac OS X computer. This allows them to add network printers, log in with their ITS account credentials, and print documents while being charged per page to funds in their pre-paid ITS account. The document provides step-by-step instructions for installing the software, adding a network printer, and printing to that printer from any internet connection on or off campus. It also explains the pay-in-advance printing payment system and how to check printing charges.
The document provides an overview of the Mac OS X user interface for beginners, including descriptions of the desktop, login screen, desktop elements like the dock and hard disk, and how to perform common tasks like opening files and folders. It also addresses frequently asked questions for Windows users switching to Mac OS X, such as where documents are stored, how to save or find documents, and what the equivalent of the C: drive is in Mac OS X. The document concludes with sections on file management tasks like creating and deleting folders, organizing files within applications, using Spotlight search, and an overview of the Dashboard feature.
This document provides a checklist for securing Mac OS X version 10.5, focusing on hardening the operating system, securing user accounts and administrator accounts, enabling file encryption and permissions, implementing intrusion detection, and maintaining password security. It describes the Unix infrastructure and security framework that Mac OS X is built on, leveraging open source software and following the Common Data Security Architecture model. The checklist can be used to audit a system or harden it against security threats.
This document summarizes a course on web design that was piloted in the summer of 2003. The course was a 3 credit course that met 4 times a week for lectures and labs. It covered topics such as XHTML, CSS, JavaScript, Photoshop, and building a basic website. 18 students from various majors enrolled. Student and instructor evaluations found the course to be very successful overall, though some improvements were suggested like ensuring proper software and pairing programming/non-programming students. The document also discusses implications of incorporating web design material into existing computer science curriculums.
Vicki Haugen McMaster is seeking a position in web design, front-end development, or digital photography. She has over 12 years of experience in front-end development using HTML and CSS, as well as expertise in Adobe Creative Suite programs like Photoshop. Her previous roles include web developer positions at Aquent and The Creative Group where she updated websites and assisted development teams.
1. A Match-Making System for Learners and Learning Objects
Harold Boley1, Virendrakumar C. Bhavsar2, David Hirtle2,
Anurag Singh2, Zhongwei Sun2, and Lu Yang2
1
Institute for Information Technology - e-Business, National Research Council of Canada (NRC)
46 Dineen Drive, Fredericton, NB E3B 9W4, Canada
harold.boley AT nrc-cnrc.gc.ca
2
Faculty of Computer Science, University of New Brunswick (UNB), P.O. Box 4400,540 Windsor Street
Gillin Hall, Fredericton, NB, E3B 5A3, Canada
{bhavsar, david.hirtle, anurag.singh, e28a1, o6c11} AT unb.ca
Abstract
We have proposed and implemented AgentMatcher, an architecture for match-making in
e-Business applications. It uses arc-labeled and arc-weighted trees to match buyers and
sellers via our novel similarity algorithm. This paper adapts the architecture for match-
making between learners and learning objects (LOs). It uses the Canadian Learning
Object Metadata (CanLOM) repository of the eduSource e-Learning project. Through
AgentMatcher’s new indexing component, known as Learning Object Metadata
Generator (LOMGen), metadata is extracted from HTML LOs for use in CanLOM.
LOMGen semi-automatically generates the LO metadata by combining a word frequency
count and dictionary lookup. A subset of these metadata terms can be selected from a
query interface, which permits adjustment of weights that express user preferences. Web-
based prefiltering is then performed over the CanLOM metadata kept in a relational
database. Using an XSLT (Extensible Stylesheet Language Transformations) translator,
the prefiltered result is transformed into an XML representation, called Weighted Object-
Oriented (WOO) RuleML (Rule Markup Language). This is compared to the WOO
RuleML representation obtained from the query interface by AgentMatcher’s core
Similarity Engine. The final result is presented as a ranked LO list with a user-specified
threshold.
Keywords: AgentMatcher, CanLOM, e-Business, e-Learning, Learning Objects, match-
making, metadata, metadata generator, RuleML
1. INTRODUCTION
1
2. We have developed the AgentMatcher system [Sarno et al. 2003] for match-making
between buyer and seller agents. The present paper describes the application of this
system for searching procurable learning objects (LOs) in an e-Learning environment.
Keywords and keyphrases are often used to describe LOs as well as learner queries in
such environments. However, such a flat representation does not lend itself to
hierarchical LO matching enabled by the Learning Object Metadata (LOM) standard and
is also not likely to reflect user preferences about the relative importance or weighting of
the parts of a LOM description. AgentMatcher takes into account both of these
dimensions — hierarchical matching and differential weighting — via tree-structured
descriptions with arc weights for the queries, enhancing the precision of LO retrieval.
In this paper we describe the Java-based AgentMatcher match-making architecture as
applied to the XML-based Canadian Learning Object Metadata (CanLOM) repository of
the Canadian eduSource project [eduSource 2004]. The CanLOM repository is built using
the LOM standard specified by the CanCore [CanCore 2004] guidelines. The Learning
Object Metadata Generator (LOMGen) extracts CanCore metadata from HTML LOs in
the domain of ‘Computing’, speeding up the process of metadata generation [Singh et al.
2004]. LOMGen-extracted terms are offered to learners for selection from a query
interface that permits convenient entry of relevant tree components and weights. Web-
based prefiltering is then performed over the CanLOM metadata kept in the relational
database of the KnowledgeAgora e-Learning repository of TeleEducation New
Brunswick (TeleEd). The prefiltered result is transformed to Weighted Object-Oriented
(WOO) RuleML [Boley 2003] via an XML-to-XML translator. Finally, this is compared
to the WOO RuleML-serialised tree obtained from the query interface using the weighted
tree similarity algorithm [Bhavsar et al. 2004] embedded in the AgentMatcher Similarity
Engine, and a percentage-ranked LO list is presented to the learner.
2. OVERVIEW
The AgentMatcher architecture can be applied to match-making [Sycara et al. 2001]
between buyer and seller agents in e-Business, e-Learning and other environments. The
core engine of AgentMatcher performs similarity computation between metadata
descriptions carried by buyer and seller agents. In the AgentMatcher instantiation for e-
Learning, "buyers" are learners and "sellers" are learning object (LO) providers. We use
the guidelines specified by CanCore to describe LOs. Thus, the match-making between
buyer and seller agents corresponds to the matching of learner queries and CanCore
descriptions.
The architecture of the AgentMatcher as adapted to e-Learning is depicted in Fig. 1,
showing the top-level retrieval and indexing components.
2
3. Retrieval Components
Prefilter parameter
(Query URI) Indexing Components
WOO RuleML
prefiltered HTML
file WOO
UI partial CanCore
user CanCore files CanCore
files
(Java) RuleML files
input files files LOMGe
Translat Prefilt LOR
Similari CANLO
n
ty or er M
Search (Java)
(XSLT) (SQL) (HTML
Engine (XML)
End (Java)
Results
user Recommended
Administrator
results
input
DATABAS
Administrator E
Keyword Table
Dataflow realized by TeleEd
Components developed by TeleEd or
a third party
Dataflow realized by UNB
Components developed by UNB/NRC Dataflow between TeleEd
and UNB/NRC
Figure 1. The AgentMatcher architecture.
There are three retrieval components: the User Interface, Similarity Engine and
Translator. The LOM Generator (LOMGen) in the mean time performs indexing to
support retrieval. Each of these four major components of the system is detailed in the
ensuing sections.
3
4. 3. USER INTERFACE
The user interface permits a user to enter as well as assign weights to search parameters
and retrieve ranked search results in a new browser window.
Figure 2. Search screen of the user interface.
As shown in Fig. 2, the search screen is split into multiple boxes reflecting the top-level
branches (‘General’, ‘Classification’, etc.) of the query tree structure; each of these boxes
contains one or more search parameters chosen from the same category as found in the
CanCore schema. Accompanying each search parameter is a slider, permitting the user to
input not only the parameter but also its corresponding weight. This weight indicates the
importance of a parameter to the user relative to other parameters within the same
category. All the weights within one box are constrained to add up to 1.0. The user is also
able to input a threshold for the search result recommendations, causing all the LOs with
a similarity value above the threshold to be considered as the recommendations.
After the user submits the advanced search request, the internal functions will be invoked
according to the dataflow in Fig. 1. First of all, a Weighted Object-Oriented RuleML
(WOO RuleML) parameter file (hereafter referred to as user.xml) is generated by the
user interface. WOO RuleML is the format required by the Similarity Engine. Then
selected search parameters are sent via a query URI to the KnowledgeAgora database
server for pre-filtering, using the database query functionality to select relevant LOs by
examining their Learning Object Metadata (LOM). The response from KnowledgeAgora
is parsed into multiple XML files. These files are translated by the Translator into WOO
4
5. RuleML files and passed to the Similarity Engine. At this point, the user.xml file is
compared with each of the LOM files translated into WOO RuleML. The final result of
the similarity computations is then displayed as a list of LOs ranked according to their
similarity to the original search parameters entered by the user (see Section 5). Only
those LOs with similarities above the threshold are recommended to the user.
4. TRANSLATOR
The translator is responsible for translating the pre-filtered LOM files from the CanLOM
repository into Weighted Object-Oriented RuleML, required by the Similarity Engine. It
defaults LOM weights to equal values (up to rounding) on all tree levels, since this e-
Learning application of AgentMatcher uses proper weights only for the query trees.
The (abbreviated) sample illustrated in Fig. 3 demonstrates the mapping between the two
formats. Extensible Stylesheet Language Transformations (XSLT), a W3C recommended
language for transforming XML documents into other XML documents, is used to
accomplish this mapping. Additional information about this translation process is
available in a separate report [Hirtle and Sun 2003]. When translation is complete, the
resulting WOO RuleML files are passed to the Similarity Engine for comparison to the
WOO RuleML representation of the search parameters specified by the user.
CanLOM XML WOO RuleML
<Cterm>
<Ctor>lom</Ctor>
...
<slot weight="0.16667">
<Ind>general</Ind>
<Cterm>
<lom> <Ctor>general_set</Ctor>
<general> ...
... <slot weight="0.33333">
<title> <Ind>title</Ind>
<string> <Ind>
Introduction to Databases Introduction to Databases
</string> </Ind>
</title> </slot>
... ...
</general> </Cterm>
... </slot>
</lom> ...
</Cterm>
Figure 3. Mapping from CanLOM XML to WOO RuleML.
5. SIMILARITY ENGINE
The Similarity Engine is responsible for computing the similarity between the query file and
prefiltered LOM files using our tree similarity algorithm [Bhavsar et al. 2004]. It constructs a
ranked list of search results and displays it in a browser window.
5
6. As shown in Fig. 4, the inputs of the Similarity Engine are the query file user.xml
generated from the user interface (as discussed in Section 3) and the translated LOM files (as
discussed in Section 4). We use our tree similarity algorithm, embedded in the Similarity
Engine, to compute, one by one, similarity values between the query and each LOM. Due to
our unique tree representation for learners and learning objects, our tree similarity algorithm
is quite different from previous work [Liu and Geiger 1999] [Wang et al. 1998]. These
similarity values are constrained to the real interval [0.0, 1.0].
Translated LOMs (XML files):
Query (XML File) generated LOM 1
from the user interface:
Query Similarity LOM 2
Engine
LOM n
Figure 4. Inputs of the Similarity Engine.
After computing the similarity between the query and LOMs, the Similarity Engine ranks all
of the LOs in descending order of similarity, graphically separating those results whose
similarity values fall below the threshold. The user will find on the top of the list the LOM
that has the highest similarity value with his/her query.
Figure 5. Snapshot of search results (low threshold).
Fig. 5 shows the HTML output for a relatively low threshold of 0.86. There are four columns
in the result table: Rank, Similarity, LOMs and LOs. The rank represents the descending
similarity order of the LOs, where highest rank corresponds to the highest similarity value.
The similarity values are displayed in the second column. The LOMs and LOs are shown in
6
7. the final two columns; clicking the link of a LOM record (e.g. WOORuleML10.xml)
displays the metadata (in XML format) corresponding to the LO. The “Go to the website”
links in the last column point to organizations’ websites that give the content of LOs.
Besides showing the search results above the threshold, we also show those that are below
the threshold in case some users want to see more LOMs and LOs. Links for these results are
displayed in white.
Figure 6. Snapshot of search results (high threshold).
Sometimes a user may input a similarity threshold that is too high, resulting in a failed
search. In this case, we do not direct users back to the search screen to adjust the threshold,
but instead give users warning that their threshold is too high and still show all the search
results that are below the threshold. Fig. 6 shows the search results in such a situation. Of
course, if users want to change inputs (e.g., keywords), they have to go back to the interface
to input again.
7
8. 6. LOM GENERATOR (LOMGen)
The process of manually entering metadata to describe an LO is a time-consuming
process. It generally requires the metadata administrator/author to be intimately familiar
with the LO content. A semi-automated process which extracts information from the LO
can alleviate the difficulties associated with this time-consuming process. LOMGen aims
at automating the metadata extraction process with minimal user intervention.
LOMGen works with LOs constituted as HTML files. LOMGen uses the Free Online
Dictionary of Computing (FOLDOC) to generate keywords and keyphrases from an LO.
The use of FOLDOC currently restricts LOMGen applicability to LOs in the domain of
‘Computing’.
As shown in Fig. 7, the LOMGen architecture consists mainly of an HTML file reader
module (which reads an LO file from a URI), an HTML parser, a word frequency
counter, a database interface module, and an XML file writer (which updates the
metadata repository with a newly generated LOM file).
1. Prompts administrator to select relevant
keyphrases and add more if required
2. Administrator provides new keyphrases if CANLOM
Learning Object
required. The vocabulary gets updated with Metadata
(Repository (LOR
additional terms as more LOs are parsed Repository
Retrieved Validated
HTML file HTML file XML file
(URI) Fills in remaining
from LOR tag values Updated
XML file
Metadata Administrator
HTML Parser
Free text (stop CANLOM
words eliminated) XML file
template
Frequency Counter 1 2
Uses template,
Most Frequent updates general
Terms identifier
update XML Generator
Synonym/Related Keywords/Keyphrases
Terms Finder Database
retrieve
(Derived from FOLDOC)
Extracted keyphrases, description, and title
Figure 7. LOMGen architecture.
LOMGen obtains the most frequent words and phrases from the content of the LO. In
addition, FOLDOC is referenced to find terms related to ‘Computing’ present in the LO.
8
9. In order to get relevant results, frequently occurring stop words such as "is", "are", "the",
and "in" are eliminated. The result is combined with information found in the HTML
"meta" tags and additional keyphrases (which may not be present in the LO) are
generated with the help of FOLDOC. These keyphrases are obtained by looking up
synonyms and related terms for words or phrases. All these words and phrases are
presented to the metadata administrator through the LOMGen Graphical User Interface
(GUI) for keyphrase selection as well as synonym and term addition. The updates made
by the administrator are stored in the keywords/keyphrases database. The newly added
terms are considered to provide better choices to the metadata administrator when
processing similar LOs.
A snapshot of the GUI presented to the metadata administrator is shown in Fig. 8. The
GUI presents a list of keywords and keyphrases that were extracted or derived from the
LO. The checkboxes present under the title “KEYPHRASE” allow the metadata
administrator to select the most important keywords or keyphrases. The textboxes under
“ADD SYNONYMS” allow the administrator to add alternate but similar terms
corresponding to the keyphrase on the left. As the metadata administrator selects
keyphrases in the GUI, the domain term dropdown listbox gets populated with the
domains for those choices. These domain terms are obtained from FOLDOC. The
metadata administrator selects the most relevant domain and FOLDOC is used to derive a
hierarchy for classifying the LO.
If an LO lacks sufficient information in the text and HTML metatags, the quality of the
keywords or keyphrases extracted by LOMGen may not be satisfactory. In such a
scenario, the GUI enables the administrator to add more terms explicitly to describe the
LO.
Finally, clicking the “OK” button generates a LOM file and posts it to the CanLOM
repository through a standard interface for posting XML files (provided by CanLOM).
9
10. Figure 8. GUI for keyphrase selection.
The LOMGen component can be used as a training module for a text summarizer that
uses machine learning techniques, with the intention of eliminating many of the
administrator inputs.
7. CONCLUSION AND FUTURE WORK
The AgentMatcher match-making system is applied to e-Learning, where learners are in
search of procurable LOs. The resulting Java-based architecture takes advantage of the
added expressiveness obtained from tree-based matching and user-assigned weights.
CanCore metadata is extracted from HTML LOs by our LOMGen indexer, speeding up
the task of metadata generation. The metadata is first prefiltered via a query URI, and
then transformed to Weighted Object-Oriented RuleML via an XSLT translator. The
results are then compared to another tree representation of the learner query as generated
by the user interface. Finally, a list of learning objects is presented to the learner in
descending order of similarity, computed by the weighted tree similarity algorithm. This
application of AgentMatcher, restricted to the ‘Computing’ domain in this project,
demonstrates enhanced precision achievable relative to standard keyword-based searches.
Generally, we showed that the AgentMatcher architecture can be easily instantiated for
e-Learning applications, where match-making between buyers and sellers in e-Business is
transferred to learners and learning object providers, respectively. The system is available
online via the page www.cs.unb.ca/agentmatcher. AgentMatcher has also been adapted to
match-making in another domain, namely technology transfer wherein buyers and sellers
can be venture capitalists and startups (visit the www.teclantic.ca portal for details).
10
11. In future, the tree similarity algorithm embedded in the Similarity Engine can be
enhanced, e.g. by adding local similarity measures. Our pairing algorithm [Sarno et al.
2003] can be modified to pair learners and learning objects. The user interface can also be
improved. The LOMGen indexing module can be enhanced by natural language
processing techniques for syntactic and semantic analysis of LOs; these techniques are
expected to improve the quality of the metadata generated and further automate the
metadata extraction process.
Acknowledgements
We thank the CANARIE eduSource Project and NSERC as well as the New Brunswick
Department of Education for their support. We also appreciate valuable comments from
referees.
References
Bhavsar, V.C., H. Boley, L. Yang, 2004, “A Weighted-Tree Similarity Algorithm for
Multi-Agent Systems in E-Business Environments”, In Proceedings of 2003 Workshop
on Business Agents and the Semantic Web, Halifax, June 14, 2003, National Research
Council of Canada, Institute for Information Technology, Fredericton, pp. 53-72, 2003.
Revised version appears in Computational Intelligence, 20(4), pp. 584-602.
Boley, H., 2003, Object-Oriented RuleML: User-Level Roles, URI-Grounded Clauses
and Order-Sorted Terms. In Schroeder, M. Wagner, G. (Eds.): Rules and Rule Markup
Languages for the Semantic, Web Springer-Verlag, Heidelberg, LNCS-2876, pp. 1-16.
CanCore, 2004: Canadian Core Learning Resource Metadata Application Profile,
www.cancore.ca.
eduSource Canada, 2004: Canadian Network of Learning Object Repositories,
www.edusource.ca/english/home_eng.html.
Hirtle, D. and Z. Sun, 2003, “CanCore WOO RuleML”, Internal Report, Faculty of
Computer Science, University of New Brunswick,
www.cs.unb.ca/agentmatcher/translators.
Liu, T. and D. Geiger, 1999, “Approximate Tree Matching and Shape Similarity”,
International Conference on Computer Vision, Kerkyra, Greece.
Sarno, R., L. Yang, V.C. Bhavsar and H. Boley, 2003, “The AgentMatcher architecture
applied to power grid transactions”, In Proceedings of the First International Workshop
on Knowledge Grid and Grid Intelligence, Halifax, Canada, pp. 92-99.
11
12. Singh, A., H. Boley and V.C. Bhavsar, 2004, “A Learning Object Metadata Generator
Applied to Computer Science Terminology,” Presented at eduSource Learning Objects
Summit, National Research Council of Canada, Fredericton, March 29-30, 2004.
Sycara, K., M. Paolucci, M. van Velsen, and J. A. Giampapa, 2001, The RETSINA MAS
infrastructure. Robotics Institute, Carnegie Mellon University, CMU-RI-TR-01-05.
Wang, J. T., B. A. Shapiro, D. Shasha, K. Zhang and K. M. Currey, 1998, “An Algorithm
for Finding the Largest Approximately Common Substructures of Two Trees”, IEEE
Transactions on Pattern Analysis and Machine Intelligence, 20(8): 889-895.
12