This document summarizes an approach to segmenting search interfaces using a two-layered hidden Markov model (HMM). The first layer uses a T-HMM to tag interface components with semantic labels like attribute-name, operator, and operand. The second layer uses an S-HMM to segment the interface into logical attributes by grouping related tagged components. The approach models an artificial designer that learns to segment interfaces by training the HMMs on manually segmented examples. It was tested on 200 biology search interfaces and showed promising results for extracting the underlying database querying semantics from the interface structure. Future work aims to improve schema extraction and domain coverage.
This document summarizes a study on using Hidden Markov Models (HMMs) for search interface segmentation. The researchers applied a two-layered HMM approach, with the first layer tagging interface components with semantic labels and the second layer segmenting the interface. Their experiments showed domain-specific HMMs performed best on interfaces from the same domain, while cross-domain HMMs captured patterns across domains. The study contributed an effective probabilistic approach to interface segmentation and found appropriate training data is key to accurate segmentation across domains.
Purpose of the data base system, data abstraction, data model, data independence, data definition
language, data manipulation language, data base manager, data base administrator, data base users,
overall structure.
ER Models, entities, mapping constrains, keys, E-R diagram, reduction E-R diagrams to tables,
generatio, aggregation, design of an E-R data base scheme.
Oracle RDBMS, architecture, kernel, system global area (SGA), data base writer, log writer, process
monitor, archiver, database files, control files, redo log files, oracle utilities.
SQL: commands and data types, data definition language commands, data manipulation commands,
data query language commands, transaction language control commands, data control language
commands.
Joins, equi-joins, non-equi-joins, self joins, other joins, aggregate functions, math functions, string
functions, group by clause, data function and concepts of null values, sub-querries, views.
PL/SQL, basics of pl/sql, data types, control structures, database access with PL/SQL, data base
connections, transaction management, data base locking, cursor management.
This document discusses database design using Entity Relationship Diagrams (ERDs). It covers how to draw ERDs using Chen's Model and Crow's Foot notations and define the basic elements of ERDs. Conversion rules are presented to convert ERDs into relational tables for one-to-one, one-to-many, and many-to-many relationships. An example is given to demonstrate drawing an ERD for a company database and converting it into relational tables.
Study on a Hybrid Segmentation Approach for Handwritten Numeral Strings in Fo...inventionjournals
This paper presents a hybrid approach to segment single- or multiple-touching handwritten numeral strings in form document, the core of which is the combined use of foreground, background and recognition analysis. The algorithm first located some feature points on both the foreground and background skeleton images containing connected numeral strings in form document. Possible segmentation paths were then constructed by matching these feature points, with an unexpected benefit of removing useless strokes. Subsequently, all these segmentation paths were validated and ranked by a recognition-based analysis, where a well-trained two-stage classifier was applied to each separated digit image to obtain its reliability. Finally, by introducing a locally optimal strategy to accelerate the recognition process, the top ranked segmentation path survived to help make a decision on whether to accept or not. Experimental results show that the proposed method can achieve a correct segmentation rate of 96.2 percent on a large dataset collected by our own.
This document discusses techniques for integrating extracted data and schemas. It begins by introducing the problems of column and instance value matching during data integration. It then describes common database integration techniques like schema matching. It also discusses linguistic, constraint-based, domain-level, and instance-level matching approaches. Finally, it covers issues specific to integrating web query interfaces, such as building a global query interface and matching interfaces through correlation mining and clustering algorithms.
The document introduces query processing and optimization in database management systems. It discusses the three main phases a query passes through: 1) parsing and translation, 2) optimization, and 3) evaluation. In the first phase, the query is converted into an internal representation like relational algebra. In the second phase, rules are applied to transform the representation into a more efficient form. In the third phase, the optimized plan is executed and results are returned. The goal is to retrieve desired information from the database in a predictable, reliable, and timely manner.
MATBASE AUTO FUNCTION NON-RELATIONAL CONSTRAINTS ENFORCEMENT ALGORITHMSijcsit
MatBase is an intelligent prototype data and knowledge base management system based on the Relational (RDM), Entity-Relationship, and (Elementary) Mathematical ((E)MDM) Data Models, built upon Relational Database Management Systems (RDBMS). ((E)MDM) has 61 constraint types, out of which21 apply to autofunctions as well. All five relational (RDM) constraint types are passed by MatBase for enforcement to the corresponding RDBMS host. All non-relational ones are enforced by MatBase through automatically generated code. This paper presents and discusses both the strategy and the implementation of MatBase autofunction non-relational constraints enforcement algorithms. These algorithms are taught to our M.Sc. students within the Advanced Databases lectures and labs, both at the Ovidius University and at the Department of Engineering in Foreign Languages, Computer Science Taught in English Stream of the Bucharest Polytechnic University, as well as successfully used by two Romanian software companies.
1. The document contains 44 questions and answers about database management systems (DBMS). It covers topics like what is a database, DBMS, data models, normalization, SQL, and more.
2. The questions range from basic definitions to more advanced concepts in database design like functional dependencies, various normal forms, and distributed database architectures.
3. Key areas covered include data definition language (DDL), data manipulation language (DML), database security, concurrency control, and distributed database architectures.
This document summarizes a study on using Hidden Markov Models (HMMs) for search interface segmentation. The researchers applied a two-layered HMM approach, with the first layer tagging interface components with semantic labels and the second layer segmenting the interface. Their experiments showed domain-specific HMMs performed best on interfaces from the same domain, while cross-domain HMMs captured patterns across domains. The study contributed an effective probabilistic approach to interface segmentation and found appropriate training data is key to accurate segmentation across domains.
Purpose of the data base system, data abstraction, data model, data independence, data definition
language, data manipulation language, data base manager, data base administrator, data base users,
overall structure.
ER Models, entities, mapping constrains, keys, E-R diagram, reduction E-R diagrams to tables,
generatio, aggregation, design of an E-R data base scheme.
Oracle RDBMS, architecture, kernel, system global area (SGA), data base writer, log writer, process
monitor, archiver, database files, control files, redo log files, oracle utilities.
SQL: commands and data types, data definition language commands, data manipulation commands,
data query language commands, transaction language control commands, data control language
commands.
Joins, equi-joins, non-equi-joins, self joins, other joins, aggregate functions, math functions, string
functions, group by clause, data function and concepts of null values, sub-querries, views.
PL/SQL, basics of pl/sql, data types, control structures, database access with PL/SQL, data base
connections, transaction management, data base locking, cursor management.
This document discusses database design using Entity Relationship Diagrams (ERDs). It covers how to draw ERDs using Chen's Model and Crow's Foot notations and define the basic elements of ERDs. Conversion rules are presented to convert ERDs into relational tables for one-to-one, one-to-many, and many-to-many relationships. An example is given to demonstrate drawing an ERD for a company database and converting it into relational tables.
Study on a Hybrid Segmentation Approach for Handwritten Numeral Strings in Fo...inventionjournals
This paper presents a hybrid approach to segment single- or multiple-touching handwritten numeral strings in form document, the core of which is the combined use of foreground, background and recognition analysis. The algorithm first located some feature points on both the foreground and background skeleton images containing connected numeral strings in form document. Possible segmentation paths were then constructed by matching these feature points, with an unexpected benefit of removing useless strokes. Subsequently, all these segmentation paths were validated and ranked by a recognition-based analysis, where a well-trained two-stage classifier was applied to each separated digit image to obtain its reliability. Finally, by introducing a locally optimal strategy to accelerate the recognition process, the top ranked segmentation path survived to help make a decision on whether to accept or not. Experimental results show that the proposed method can achieve a correct segmentation rate of 96.2 percent on a large dataset collected by our own.
This document discusses techniques for integrating extracted data and schemas. It begins by introducing the problems of column and instance value matching during data integration. It then describes common database integration techniques like schema matching. It also discusses linguistic, constraint-based, domain-level, and instance-level matching approaches. Finally, it covers issues specific to integrating web query interfaces, such as building a global query interface and matching interfaces through correlation mining and clustering algorithms.
The document introduces query processing and optimization in database management systems. It discusses the three main phases a query passes through: 1) parsing and translation, 2) optimization, and 3) evaluation. In the first phase, the query is converted into an internal representation like relational algebra. In the second phase, rules are applied to transform the representation into a more efficient form. In the third phase, the optimized plan is executed and results are returned. The goal is to retrieve desired information from the database in a predictable, reliable, and timely manner.
MATBASE AUTO FUNCTION NON-RELATIONAL CONSTRAINTS ENFORCEMENT ALGORITHMSijcsit
MatBase is an intelligent prototype data and knowledge base management system based on the Relational (RDM), Entity-Relationship, and (Elementary) Mathematical ((E)MDM) Data Models, built upon Relational Database Management Systems (RDBMS). ((E)MDM) has 61 constraint types, out of which21 apply to autofunctions as well. All five relational (RDM) constraint types are passed by MatBase for enforcement to the corresponding RDBMS host. All non-relational ones are enforced by MatBase through automatically generated code. This paper presents and discusses both the strategy and the implementation of MatBase autofunction non-relational constraints enforcement algorithms. These algorithms are taught to our M.Sc. students within the Advanced Databases lectures and labs, both at the Ovidius University and at the Department of Engineering in Foreign Languages, Computer Science Taught in English Stream of the Bucharest Polytechnic University, as well as successfully used by two Romanian software companies.
1. The document contains 44 questions and answers about database management systems (DBMS). It covers topics like what is a database, DBMS, data models, normalization, SQL, and more.
2. The questions range from basic definitions to more advanced concepts in database design like functional dependencies, various normal forms, and distributed database architectures.
3. Key areas covered include data definition language (DDL), data manipulation language (DML), database security, concurrency control, and distributed database architectures.
The document discusses the relational database model. It begins by defining key terms like data, information, database, and DBMS. It then explains the relational model proposed by E.F. Codd, showing an example student database. Codd's rules for relational databases are listed. Types of database anomalies and keys like super keys, candidate keys, and foreign keys are also defined. The advantages of relational databases include structural independence and conceptual simplicity. Disadvantages include increased hardware needs and the potential for poor database design.
This document discusses the SQL language for relational databases. It covers the background and history of SQL, the SQL standards, and the key statements and features of SQL including data definition, data types, schema and table creation, attributes, constraints, keys and referential integrity. The document provides examples of SQL statements and clauses to define schemas, tables, attributes, primary keys, foreign keys and other constraints.
This document contains instructions for an assignment for a Web Technologies course. It includes 6 questions related to TCP vs UDP, features of XML, components of an XML processor, fetching data from XML to HTML, categories of PHP operators, and Active Server Pages (ASP). The questions range from short definitions and comparisons to longer explanations and examples.
Chapter-2 Database System Concepts and ArchitectureKunal Anand
This document provides an overview of database management systems concepts and architecture. It discusses different data models including hierarchical, network, relational, entity-relationship, object-oriented, and object-relational models. It also describes the 3-schema architecture with external, conceptual, and internal schemas and explains components of a DBMS including users, storage and query managers. Finally, it covers database languages like DDL, DML, and interfaces like menu-based, form-based and graphical user interfaces.
Logical database design and the relational model(database)welcometofacebook
The document discusses logical database design and the relational model, including transforming entity-relationship diagrams into relations through a process called normalization to eliminate data anomalies and achieve well-structured relations. It defines the relational data model, explains how to map entities and relationships to tables, and covers the three normal forms to validate and improve table structure through analysis of functional dependencies between attributes.
The document discusses database design, including transforming entity-relationship diagrams into normalized relations, integrating different user views, choosing data storage formats, designing efficient database tables, file organization, and indexes. It covers key database concepts such as relations, primary keys, normalization, foreign keys, and data types. The goal of database design is to structure data in stable, normalized tables that are efficient for storage and access.
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.
Immune-Inspired Method for Selecting the Optimal Solution in Semantic Web Ser...IJwest
The increasing interest in developing efficient and effective optimization techniques has conducted researchers to turn their attention towards biology. It has been noticed that biology offers many clues for designing novel optimization techniques, these approaches exhibit self-organizing capabilities and permit the reachability of promising solutions without the existence of a central coordinator. In this paper we handle the problem of dynamic web service composition, by using the clonal selection algorithm. In order to assess the optimality rate of a given composition, we use the QOS attributes of the services involved in the workflow as well as, the semantic similarity between these components. The experimental evaluation shows that the proposed approach has a better performance in comparison with other approaches such as the genetic algorithm.
Cleveree: an artificially intelligent web service for Jacob voice chatbotTELKOMNIKA JOURNAL
Jacob is a voice chatbot that use Wit.ai to get the context of the question and give an answer based on that context. However, Jacob has no variation in answer and could not recognize the context well if it has not been learned previously by the Wit.ai. Thus, this paper proposes two features of artificial intelligence (AI) built as a web service: the paraphrase of answers using the Stacked Residual LSTM model and the question summarization using Cosine Similarity with pre-trained Word2Vec and TextRank algorithm. These two features are novel designs that are tailored to Jacob, this AI module is called Cleveree. The evaluation of Cleveree is carried out using the technology acceptance model (TAM) method and interview with Jacob admins. The results show that 79.17% of respondents strongly agree that both features are useful and 72.57% of respondents strongly agree that both features are easy to use.
Database Design and the ER Model, Indexing and HashingPrabu U
This document provides an overview of database design and the entity-relationship (ER) model. It discusses the database design process, including initial, conceptual, logical, and physical design phases. It then describes the key concepts of the ER model, including entities, attributes, relationships, cardinalities, participation constraints, and keys. The document explains how to design ER diagrams and how to remove redundant attributes. It provides examples of one-to-one, one-to-many, many-to-one, and many-to-many relationships. Finally, it demonstrates how to represent complex attributes like composite, multi-valued, and derived attributes in an ER diagram.
This document discusses the limitations of traditional database technologies and introduces associative technology as an evolution in database storage and retrieval. Some key limitations of traditional databases include disparate data sources, lack of timely information, high costs, and complex systems. Associative technology models data in an 'n' normal form that maps data relationally like human memory. It stores single instances of data values and uses bidirectional pointers to associate related data. This approach eliminates data redundancy and allows for fast, flexible querying of complex, large datasets.
The document discusses conceptual data modeling and entity-relationship (ER) modeling. It describes the key concepts in ER modeling including entities, attributes, relationships, cardinality, participation, and relationship types. It provides examples of how to model different types of relationships, attributes, and entities. The goal of conceptual modeling is to build an abstract yet rigorous model of an organization's data to help communicate requirements and ensure quality.
Fundamentals of Database Systems questions and answers with explanation for fresher's and experienced for interview, competitive examination and entrance test.
Availability Assessment of Software Systems Architecture Using Formal ModelsEditor IJCATR
There has been a significant effort to analyze, design and implement the information systems to process the information and data, and solve various problems. On the one hand, complexity of the contemporary systems, and eye-catching increase in the variety and volume of information has led to great number of the components and elements, and more complex structure and organization of the information systems. On the other hand, it is necessary to develop the systems which meet all of the stakeholders' functional and non-functional requirements. Considering the fact that evaluation and assessment of the aforementioned requirements - prior to the design and implementation phases - will consume less time and reduce costs, the best time to measure the evaluable behavior of the system is when its software architecture is provided. One of the ways to evaluate the architecture of software is creation of an executable model of architecture.
The present research used availability assessment and took repair, maintenance and accident time parameters into consideration. Failures of software and hardware components have been considered in the architecture of software systems. To describe the architecture easily, the authors used Unified Modeling Language (UML). However, due to the informality of UML, they utilized Colored Petri Nets (CPN) for assessment too. Eventually, the researchers evaluated a CPN-based executable model of architecture through CPN-Tools.
The document presents an ensemble model for chunking natural language text that combines a transformer model (RoBERTa) with a bidirectional LSTM and CNN model. The authors train these models on common chunking datasets like CoNLL 2000 and English Penn Treebank. They find that by using an ensemble of the transformer and RNN-CNN models, which compensate for each other's weaknesses, they are able to achieve state-of-the-art results on chunking, with an F1 score of 97.3% on CoNLL 2000, exceeding previous work. The transformer model provides attention-based contextual embeddings while the RNN-CNN model uses custom embeddings including POS tags to improve accuracy on tags that the transformer model struggles with.
The document provides an overview of Query-by-Example (QBE) and Datalog, two relational query languages. QBE allows graphical queries to be expressed "by example" using relation templates. It supports queries on single and multiple relations, negation, conditions, ordering results, and aggregate functions. Datalog is a logic-based query language based on rules that define views. It allows recursion and negation. Key features include safety and the power of recursive queries.
A database management system (DBMS) is system software that allows for the creation, management, and use of databases, making it easier to create, retrieve, update and manage large amounts of data in an organized manner. The document discusses the definition, importance, implementation, requirements, and challenges of a DBMS, as well as entity relationship diagrams, modeling, and security concepts related to databases. In conclusion, a DBMS is an effective system for systematic data management that is widely used around the world.
The document discusses database design and normalization. It begins by describing different design alternatives such as using larger or smaller schemas. It then covers first normal form (1NF), which requires attributes to be atomic and domains to be indivisible. Second normal form (2NF) and third normal form (3NF) are introduced to further reduce anomalies. The document also discusses functional dependencies, normal forms like Boyce-Codd normal form (BCNF), decomposition using functional dependencies, and closure of attribute sets. Overall, the document provides an overview of relational database design principles and normalization techniques.
The document discusses the relational data model and its key concepts. The relational model represents a database as a collection of relations (tables). Each row in a relation represents a tuple of related data values. Attributes describe the columns and domains define the possible values for each attribute. Relations have schemas that define the relation name and attributes. Relation states contain sets of tuples that must satisfy integrity constraints defined on the schema.
This dissertation proposal outlines a system that allows non-technical users to design and evolve databases by modeling their data needs through customizable forms. The key goals are to provide an easy-to-use interface for form design, and mapping algorithms that translate user-designed forms into high-quality databases. A preliminary evaluation with nurses found the form modeling interface effective and efficient. Mapping experiments successfully translated forms into databases that matched expert-designed standards. Future work includes usability studies varying form and database complexity, and exploring enhancements to mapping and merging algorithms.
The document discusses the relational database model. It begins by defining key terms like data, information, database, and DBMS. It then explains the relational model proposed by E.F. Codd, showing an example student database. Codd's rules for relational databases are listed. Types of database anomalies and keys like super keys, candidate keys, and foreign keys are also defined. The advantages of relational databases include structural independence and conceptual simplicity. Disadvantages include increased hardware needs and the potential for poor database design.
This document discusses the SQL language for relational databases. It covers the background and history of SQL, the SQL standards, and the key statements and features of SQL including data definition, data types, schema and table creation, attributes, constraints, keys and referential integrity. The document provides examples of SQL statements and clauses to define schemas, tables, attributes, primary keys, foreign keys and other constraints.
This document contains instructions for an assignment for a Web Technologies course. It includes 6 questions related to TCP vs UDP, features of XML, components of an XML processor, fetching data from XML to HTML, categories of PHP operators, and Active Server Pages (ASP). The questions range from short definitions and comparisons to longer explanations and examples.
Chapter-2 Database System Concepts and ArchitectureKunal Anand
This document provides an overview of database management systems concepts and architecture. It discusses different data models including hierarchical, network, relational, entity-relationship, object-oriented, and object-relational models. It also describes the 3-schema architecture with external, conceptual, and internal schemas and explains components of a DBMS including users, storage and query managers. Finally, it covers database languages like DDL, DML, and interfaces like menu-based, form-based and graphical user interfaces.
Logical database design and the relational model(database)welcometofacebook
The document discusses logical database design and the relational model, including transforming entity-relationship diagrams into relations through a process called normalization to eliminate data anomalies and achieve well-structured relations. It defines the relational data model, explains how to map entities and relationships to tables, and covers the three normal forms to validate and improve table structure through analysis of functional dependencies between attributes.
The document discusses database design, including transforming entity-relationship diagrams into normalized relations, integrating different user views, choosing data storage formats, designing efficient database tables, file organization, and indexes. It covers key database concepts such as relations, primary keys, normalization, foreign keys, and data types. The goal of database design is to structure data in stable, normalized tables that are efficient for storage and access.
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.
Immune-Inspired Method for Selecting the Optimal Solution in Semantic Web Ser...IJwest
The increasing interest in developing efficient and effective optimization techniques has conducted researchers to turn their attention towards biology. It has been noticed that biology offers many clues for designing novel optimization techniques, these approaches exhibit self-organizing capabilities and permit the reachability of promising solutions without the existence of a central coordinator. In this paper we handle the problem of dynamic web service composition, by using the clonal selection algorithm. In order to assess the optimality rate of a given composition, we use the QOS attributes of the services involved in the workflow as well as, the semantic similarity between these components. The experimental evaluation shows that the proposed approach has a better performance in comparison with other approaches such as the genetic algorithm.
Cleveree: an artificially intelligent web service for Jacob voice chatbotTELKOMNIKA JOURNAL
Jacob is a voice chatbot that use Wit.ai to get the context of the question and give an answer based on that context. However, Jacob has no variation in answer and could not recognize the context well if it has not been learned previously by the Wit.ai. Thus, this paper proposes two features of artificial intelligence (AI) built as a web service: the paraphrase of answers using the Stacked Residual LSTM model and the question summarization using Cosine Similarity with pre-trained Word2Vec and TextRank algorithm. These two features are novel designs that are tailored to Jacob, this AI module is called Cleveree. The evaluation of Cleveree is carried out using the technology acceptance model (TAM) method and interview with Jacob admins. The results show that 79.17% of respondents strongly agree that both features are useful and 72.57% of respondents strongly agree that both features are easy to use.
Database Design and the ER Model, Indexing and HashingPrabu U
This document provides an overview of database design and the entity-relationship (ER) model. It discusses the database design process, including initial, conceptual, logical, and physical design phases. It then describes the key concepts of the ER model, including entities, attributes, relationships, cardinalities, participation constraints, and keys. The document explains how to design ER diagrams and how to remove redundant attributes. It provides examples of one-to-one, one-to-many, many-to-one, and many-to-many relationships. Finally, it demonstrates how to represent complex attributes like composite, multi-valued, and derived attributes in an ER diagram.
This document discusses the limitations of traditional database technologies and introduces associative technology as an evolution in database storage and retrieval. Some key limitations of traditional databases include disparate data sources, lack of timely information, high costs, and complex systems. Associative technology models data in an 'n' normal form that maps data relationally like human memory. It stores single instances of data values and uses bidirectional pointers to associate related data. This approach eliminates data redundancy and allows for fast, flexible querying of complex, large datasets.
The document discusses conceptual data modeling and entity-relationship (ER) modeling. It describes the key concepts in ER modeling including entities, attributes, relationships, cardinality, participation, and relationship types. It provides examples of how to model different types of relationships, attributes, and entities. The goal of conceptual modeling is to build an abstract yet rigorous model of an organization's data to help communicate requirements and ensure quality.
Fundamentals of Database Systems questions and answers with explanation for fresher's and experienced for interview, competitive examination and entrance test.
Availability Assessment of Software Systems Architecture Using Formal ModelsEditor IJCATR
There has been a significant effort to analyze, design and implement the information systems to process the information and data, and solve various problems. On the one hand, complexity of the contemporary systems, and eye-catching increase in the variety and volume of information has led to great number of the components and elements, and more complex structure and organization of the information systems. On the other hand, it is necessary to develop the systems which meet all of the stakeholders' functional and non-functional requirements. Considering the fact that evaluation and assessment of the aforementioned requirements - prior to the design and implementation phases - will consume less time and reduce costs, the best time to measure the evaluable behavior of the system is when its software architecture is provided. One of the ways to evaluate the architecture of software is creation of an executable model of architecture.
The present research used availability assessment and took repair, maintenance and accident time parameters into consideration. Failures of software and hardware components have been considered in the architecture of software systems. To describe the architecture easily, the authors used Unified Modeling Language (UML). However, due to the informality of UML, they utilized Colored Petri Nets (CPN) for assessment too. Eventually, the researchers evaluated a CPN-based executable model of architecture through CPN-Tools.
The document presents an ensemble model for chunking natural language text that combines a transformer model (RoBERTa) with a bidirectional LSTM and CNN model. The authors train these models on common chunking datasets like CoNLL 2000 and English Penn Treebank. They find that by using an ensemble of the transformer and RNN-CNN models, which compensate for each other's weaknesses, they are able to achieve state-of-the-art results on chunking, with an F1 score of 97.3% on CoNLL 2000, exceeding previous work. The transformer model provides attention-based contextual embeddings while the RNN-CNN model uses custom embeddings including POS tags to improve accuracy on tags that the transformer model struggles with.
The document provides an overview of Query-by-Example (QBE) and Datalog, two relational query languages. QBE allows graphical queries to be expressed "by example" using relation templates. It supports queries on single and multiple relations, negation, conditions, ordering results, and aggregate functions. Datalog is a logic-based query language based on rules that define views. It allows recursion and negation. Key features include safety and the power of recursive queries.
A database management system (DBMS) is system software that allows for the creation, management, and use of databases, making it easier to create, retrieve, update and manage large amounts of data in an organized manner. The document discusses the definition, importance, implementation, requirements, and challenges of a DBMS, as well as entity relationship diagrams, modeling, and security concepts related to databases. In conclusion, a DBMS is an effective system for systematic data management that is widely used around the world.
The document discusses database design and normalization. It begins by describing different design alternatives such as using larger or smaller schemas. It then covers first normal form (1NF), which requires attributes to be atomic and domains to be indivisible. Second normal form (2NF) and third normal form (3NF) are introduced to further reduce anomalies. The document also discusses functional dependencies, normal forms like Boyce-Codd normal form (BCNF), decomposition using functional dependencies, and closure of attribute sets. Overall, the document provides an overview of relational database design principles and normalization techniques.
The document discusses the relational data model and its key concepts. The relational model represents a database as a collection of relations (tables). Each row in a relation represents a tuple of related data values. Attributes describe the columns and domains define the possible values for each attribute. Relations have schemas that define the relation name and attributes. Relation states contain sets of tuples that must satisfy integrity constraints defined on the schema.
This dissertation proposal outlines a system that allows non-technical users to design and evolve databases by modeling their data needs through customizable forms. The key goals are to provide an easy-to-use interface for form design, and mapping algorithms that translate user-designed forms into high-quality databases. A preliminary evaluation with nurses found the form modeling interface effective and efficient. Mapping experiments successfully translated forms into databases that matched expert-designed standards. Future work includes usability studies varying form and database complexity, and exploring enhancements to mapping and merging algorithms.
5 tips on how to select a prom for your study presentation notesKeith Meadows
The document provides 5 tips for selecting a patient reported outcome measure (PROM) for a study:
1. Always have a clear hypothesis about what you want to measure to help identify the appropriate PROM.
2. Ensure the content and individual items of the PROM are relevant to the patient population and disease being studied.
3. Consider if the PROM will be acceptable to complete for participants, considering length, time, and design.
4. Select a PROM that has been developed scientifically with evidence of reliability and validity.
5. Be able to correctly interpret the PROM data and results, and consider collaborating with an expert if needed.
This document presents a multi-level methodology for developing UML sequence diagrams (SQDs) in a systematic way. The methodology has three levels - the object framework level, responsibility assignment level, and visual pattern level. Each level breaks the SQD development process into discrete stages and provides guidelines to help avoid common errors. The goal is to serve as an easy-to-use reference for novice SQD modelers to develop correct and consistent SQDs.
Durante lo sviluppo e test di un plugin per Spoon, che ricordo essere l’ambiente dedicato al disegno dei processi ETL di Kettle, potrebbe essere utile avviare delle sessioni di debug utili per l’individuazione di eventuali errori (bug) rilevati. In questo breve articolo vedremo come sia possibile per un plugin Kettle, avviare una sessione di debug dal nostro ambiente di sviluppo, che ipotizziamo, essere Eclipse.
The document discusses search interface understanding (SIU), which involves representing, parsing, segmenting, and evaluating search interfaces on the deep web. SIU is challenging because search interfaces are designed autonomously without standard structures. The document outlines the SIU process and key challenges, such as interfaces having no defined boundaries for segmenting semantically related components. Techniques for SIU include rules, heuristics, and machine learning.
All’interno del ciclo di vita di un progetto software reputo importante la percentuale di tempo dedicata a redigere la documentazione di progetto, a volte, anzi direi molto di frequente la percentuale di tempo dedicata a quest’attività, è intorno allo zero.
Mike Thelwall is a professor known for his research in the field of webometrics. He received his PhD in mathematics and leads the Statistical Cybermetrics Research Group. Webometrics involves the quantitative analysis of web phenomena such as link analysis, search engine evaluation, and web citation analysis. Thelwall's research has explored using webometrics to study the dissemination of scholarly research and evaluate universities. He has emphasized the need for conceptual frameworks and methodologies to interpret webometrics results and address challenges like the size and changing nature of the web.
Clinicians rely on health information technologies (HITs) for clinical data collection, but current HITs are inflexible and inconsistent with clinicians' needs. The researchers propose a flexible electronic health record (fEHR) system to allow clinicians to easily modify the system based on their changing data collection needs. The fEHR uses a form-based interface for clinicians to design forms, generates a corresponding form tree structure, and designs a high-quality database from the tree. A user study with 5 nurses found they could effectively replicate needs in the system and their efficiency and understanding improved over two rounds of tasks of increasing complexity. The researchers conclude the fEHR has potential to reduce HIT problems and that the database design
You're invited! Commemorative Dinner--Friday, January 28, 2011, at the Desert Diamond Casino (Pima Mine Rd.) & Treaty Exhibit Opening--Wednesday, February 2, 2011, at the Arizona State Museum.
The document announces the 4th Annual Segundo de Febrero Commemorative Dinner to recognize February 2, 1848, the date the Treaty of Guadalupe Hidalgo was signed establishing the border between the US and Mexico and creating the Mexican American community. The event will be held on January 30, 2010 at Desert Diamond Casino featuring a keynote speaker and award ceremony with proceeds benefiting Amistades Inc, a nonprofit for Latino substance abuse prevention. Attendees can RSVP and purchase tickets by January 15th.
Now iPad 2 is firmly in the grasp of our hands, we have decided to provide a dose of iPad 3 features round up. Although iPad 2 did not came as a big surprise to us, thanks to early leaks from Apple related blogs. Here is all we know about iPad 3 Features Rumors so far
Credit: http://www.iphonejailbreakfaq.com/ipad-3-features-round-up-rumors/
There's a Customer Out There with a Bullet for You: Understanding Your CustomersEvan Hamilton
My FailCon 2010 presentation on how not understanding your customers will kill your company faster than anything else.
Want to better understand your customers? Read this and then sign up for http://www.uservoice.com
Career portfolio which illustrates multi-media marketing, search engine marketing and optimization, strategic research and Web analytics accomplishments.
This document describes using a Hidden Markov Model (HMM) approach to segment deep web search interfaces. The HMM acts as an artificial designer that can determine segment boundaries and label components based on acquired knowledge. A two-layered HMM is employed, with the first layer assigning semantic labels and the second layer segmenting the interface. The approach outperforms previous heuristic methods, achieving a 10% improvement in segmentation accuracy. Future work involves extracting more schema details, testing on other domains, and exploring alternative training algorithms.
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.
This document summarizes a research paper on using clustering approaches to improve the discovery of semantic web services. It begins by defining semantic web services and semantic similarity measures. It then discusses using clustering to eliminate irrelevant services from a collection before applying semantic algorithms. Specifically, it proposes a clustering probabilistic semantic approach (CPLSA) that filters services based on compatibility with a query before clustering the remaining services into semantically related groups using probabilistic latent semantic analysis (PLSA). The document concludes by discussing applications of approximate semantics and challenges in scaling semantic algorithms.
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.
Zhao huang deep sim deep learning code functional similarityitrejos
Measuring code similarity is fundamental for many software engineering
tasks, e.g., code search, refactoring and reuse. However,
most existing techniques focus on code syntactical similarity only,
while measuring code functional similarity remains a challenging
problem. In this paper, we propose a novel approach that encodes
code control flow and data flow into a semantic matrix in which
each element is a high dimensional sparse binary feature vector,
and we design a new deep learning model that measures code functional
similarity based on this representation. By concatenating
hidden representations learned from a code pair, this new model
transforms the problem of detecting functionally similar code to
binary classification, which can effectively learn patterns between
functionally similar code with very different syntactics.
With these components in place, we present the Data
Science Machine — an automated system for generating
predictive models from raw data. It starts with a relational
database and automatically generates features to be used
for predictive modeling.
ArtForm - Dynamic analysis of JavaScript validation in web forms - PosterDBOnto
ArtForm is a tool that uses concolic analysis and symbolic execution to dynamically analyze JavaScript validation in web forms. It infers integrity constraints, models hidden data, and improves search and data extraction. It works by controlling a WebKit browser to execute code symbolically while tracking concrete and symbolic values. Challenges include event handler dependencies, implied constraints, and JavaScript semantics that are difficult to model. Future work focuses on handling more patterns and constraints and targeting exploration of interesting parts of form trees.
1) The document discusses a review of semantic approaches for nearest neighbor search. It describes using an ontology to add a semantic layer to an information retrieval system to relate concepts using query words.
2) A technique called spatial inverted index is proposed to locate multidimensional information and handle nearest neighbor queries by finding the hospitals closest to a given address.
3) Several semantic approaches are described including using clustering measures, specificity measures, link analysis, and relation-based page ranking to improve search and interpret hidden concepts behind keywords.
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 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.
This document discusses techniques for integrating web query interfaces and schemas. It begins with an introduction to information integration and database integration, including schema matching. It then covers pre-processing techniques used for integration like tokenization and stemming. Schema-level matching techniques are discussed like name, description, and constraint-based approaches. Domain and instance-level matching uses value characteristics. Composite domains are handled by detecting delimiters. Similarities from different match indicators can be combined. Web query interface integration is introduced, with the problem being identifying synonym attributes. Schema matching is framed as correlation mining, covering group discovery, match discovery, and matching selection. A clustering approach to 1:1 matching is also presented.
Amit P. Sheth, “Relationships at the Heart of Semantic Web: Modeling, Discovering, Validating and Exploiting Complex Semantic Relationships,” Keynote at the 29th Conference on Current Trends in Theory and Practice of Informatics (SOFSEM 2002), Milovy, Czech Republic, November 22–29, 2002.
Keynote: http://www.sofsem.cz/sofsem02/keynote.html
Related paper: http://knoesis.wright.edu/?q=node/2063
Web Information Extraction Learning based on Probabilistic Graphical ModelsGUANBO
The document describes a graphical model for jointly extracting and resolving product attributes from web pages. The model uses a Dirichlet process prior to handle an unlimited number of attributes. Variational inference is used to approximate the intractable posterior distribution. Experimental results on four domains show the model achieves good performance on attribute extraction and resolution without supervision.
The previous research has focused on quick and efficient generation of wrappers; the
development of tools for wrapper maintenance has received less attention. This is an important research
problem because Web sources often change in ways that prevent the wrappers from extracting data
correctly. Present an efficient algorithm that extract unstructured data to structural data from web. The
wrapper verification system detects when a wrapper is not extracting correct data, usually because the
Web source has changed its format. The Verification framework automatically recovers data using
Dimension Reduction Techniques from changes in the Web source by identifying data on Web pages.
After apply wrapped data to One Class Classification in Numerical features for avoid classification
problem. Finally, the result data apply in Top-K query for provide best rank based on probabilities
scores. Wrapper verification system relies on one-class classification techniques to beat previous
weaknesses to identify the problem by analysing both the signature and the classifier output. If there are
sufficient mislabelled slots, a technique to find a pattern could be explored.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
call for paper 2012, hard copy of journal, research paper publishing, where to publish research paper,
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
How to store state definitions including boolean logic decompositions into a relational structure and integrate with the state definitions for applications.
FEATURES MATCHING USING NATURAL LANGUAGE PROCESSINGIJCI JOURNAL
This document summarizes a research paper that proposes using a combination of Natural Language Processing and statistical models to match features between different datasets. Specifically, it uses BERT (Bidirectional Encoder Representations from Transformers), a pretrained NLP model, in parallel with Jaccard similarity to measure similarity between feature lists. The hybrid approach reduces time required for manual feature matching compared to previous methods. The paper describes preprocessing data, generating embeddings with BERT, calculating similarity scores with BERT and Jaccard, and outputting top matches above a threshold. It provides example results matching house sales and movie metadata features. The hybrid approach leverages strengths of BERT's semantic understanding and Jaccard's flexibility for special characters.
Advance Frameworks for Hidden Web Retrieval Using Innovative Vision-Based Pag...IOSR Journals
The document proposes an innovative vision-based page segmentation (IVBPS) algorithm to improve hidden web content extraction. It aims to overcome limitations of existing approaches that rely heavily on HTML structure. IVBPS extracts blocks from the visual representation of a page and clusters them to segment the page semantically. It uses layout features like position and appearance to locate data regions and extract records. The algorithm analyzes the entire page structure rather than local regions, allowing it to retain content DOM tree methods may discard. This is expected to significantly improve hidden web extraction performance.
Similar to Two Layered HMMs for Search Interface Segmentation (20)
Advance Frameworks for Hidden Web Retrieval Using Innovative Vision-Based Pag...
Two Layered HMMs for Search Interface Segmentation
1. 2-Layered HMMs for Search Interface Segmentation Ritu Khare (Under the Supervision of Dr Yuan An, Assistant Professor, iSchool) 1
2. Order of Presentation 2 Background Deep Web What is Search Interface Understanding? What is Interface Segmentation? Why is Segmentation Challenging? Our Approach for Segmentation Interface Representation HMM: The Artificial Designer 2-Layered Approach Architecture Experimentation Parameters Result Contributions Future Work References
3. Background: Deep Web What is Deep Web: The data that exists on the Web but is not returned by search engines through traditional crawling and indexing. The primary way to access this data is by filling up HTML forms on search interfaces. Characteristics[6] :A large proportion of structured databases; Diversity of domains; and its ; Growing scale Researchers have many goals for the deep Web: design intra-domain meta-search engines [22, 8, 15, 5, 21] increase content visibility on existing search engines [17, 12] derive ontologies from search interfaces [1]. A pre-requisite to attain these goals is an understanding of the search interfaces (slide 4). In this project, we propose an approach to address the segmentation(slide 5) portion of the problem of search interface understanding. 3
4. Background: What is Search Interface Understanding? 4 Understanding semantics of a search interface (shown in figure) is an intricate process [4] . It involves 4 stages. Representation: A suitable interface representation scheme is chosen; semantic labels (slide 8) to be assigned to interface components are decided. An interface component is any text or HTML form element (textbox, textarea, selection list, radiobutton, checkbox, file input) that exists inside an HTML form. Parsing: Components are parsed into a suitable structure. Segmentation: The interface components are assigned semantic labels , and related components are grouped together The questions like “Which surrounding text is associated with which form element?” (In figure 2, “Gene ID” is associated with the textbox placed next) are also answered in this stage. Segment-processing: Additional information, such as domain, constraints, and data type, about each segment component is extracted.
5. What is Interface Segmentation? 5 This project focuses on Segmentation, the 3rd stage of this process. Figure shows a segmented interface. The related components are grouped together. The left segment has 7 components. The right segment has 4 components (“cM Position:”, selection list, textbox, and “e.g., “10.0-40.0””).
6. Why is Segmentation Challenging? From a user’s (or designer’s) standpoint, By looking at the visual arrangement of components, and based on past experiences, the user creates a logical boundary around the related components as they appear to belong to the same atomic query. On the other hand, a machine is unable to “see” a segment due to the following reasons: The components that are visually close to each other might be located very far apart in the HTML source code, A machine does not implicitly have any search experience that can be leveraged to identify a segment boundary. This project aims to investigate whether a machine can “learn” how to understand and segment an interface. Existing works have two shortcomings: they [9,13,17] do not group all related components together i.e. do not create complete segments. they [23, 7] use rules and heuristics to segment a search interface. These techniques have problems in handling scalability and heterogeneity [10]. 6
7. Our Approach for Segmentation We incorporate the first-hand implicit knowledge using which a human designer is assumed to have designed an interface. This is accomplished by designing an artificial designer using Hidden Markov Models (refer to week 9’s slides on HMM introduction). We visualize segmentation as a two-folded problem Identification of boundaries of logical attributes (slide 9) Assignment of semantic labels (attribute-name, operator, and operand described in slide 9) to interface components. 7
8. Interface Representation 8 In figure, each component of the lower segment is marked with a label, which we term as a semantic label. The semantic label for a particular component denotes the meaning of the component from a user’s or designer’s standpoint. Search Entity Logical Attribute Logical Attribute Operand Operator Attribute-name
9. Interface Representation Attribute-name: Attribute-name denotes the criteria available for searching a particular entity, e.g. the entity “Genes” can be searched by “Gene ID” and by “Gene Name”. Operand: An attribute-name is usually associated with operand(s), the value(s) entered by the user that is(are) matched against the corresponding field value(s) in the underlying database. Operator: The user may also be given an option of specifying the operator that further qualifies an operand. Filling up an HTML form is similar to writing SQL queries. Assuming the underlying database table name is “Gene”, the SQL queries for figure would be: SELECT * FROM Gene WHERE Gene_ID= ‘PF11_0344’; SELECT * FROM Gene WHERE Gene_Name LIKE ‘maggie’; Logical Attribute: The predicate in the WHERE clause of each query is created by a group of related components. We combine the semantic roles (attribute-name, operator(s), and operand(s)) of these components to create a composite semantic label called logical attribute. Our approach assumes that a segment corresponds to a logical attribute. 9
10. HMM: The Artificial Designer 10 We assume that an HMM can act like a human designer who has the ability to design an interface using acquired knowledge and to determine (decode) the segment boundaries and semantic labels of components. The designing process is similar to statistically choosing one component from a bag of components (a superset of all possible components) and placing it on the Web page while keeping the semantic role (attribute-name, operand, or operator) of the component in mind. Knowledge of Semantic Labels Bag of Components Search Interface Designing 2-Layered HMM (Artificial Designer) Segments & Tagged Components Decoding
11. HMM: The Artificial Designer While the components are observable, their semantic roles appear hidden to a machine. The proceeding of one semantic label by another is similar to the transitioning of HMM states. In the figure, Ovals=states (semantic labels); Rectangles= emitted symbols (components). The designing ability is provided by training the HMM with suitable algorithms. Once an HMM is trained, it can be used for the decoding process i.e. for explaining the design of a given search interface. 11 Attribute Name Operand Operator Attribute Name Operand Text (Gene ID) Textbox Text (Gene Name) RadioButton Group Textbox
12. 2-Layered HMM The problem of decoding that we address in this paper is two-folded involving segmentation as well as assignment of semantic labels to components. Hence, we employ a layered HMM [14] with 2 layers. The first layer T-HMM tags each component with appropriate semantic labels (attribute-name, operator, and operand). The second layer S-HMM segments the interface into logical attributes. 12
13. 2-Layered Approach Architecture DOM-TREE PARSING Training Interfaces T-HMM Manually tagged State Sequences T-HMM Specs Test interfaces Predicted State Sequences T-HMM TRAINING T-HMM TESTING S-HMM S-HMM Specs Test interfaces S-HMM TRAINING Manually tagged State Sequences S-HMM TESTING Predicted State Sequences 13
14. Experimentation Parameters Data-Set: 200 interfaces (NAR collection) http://www3.oup.co.uk/nar/database/c/ Parsing: DOM-trees [3] of components. Trees were traversed in the depth-first search order . Testing and Training Data: The examples were randomly divided into 20 equal-sized sets. We conducted 20 experiments each having 190 training and 10 testing examples. Testing and Training Algorithms: In both layers, training and testing were performed using Maximum Likelihood method and Viterbi algorithm respectively. 14
16. Contributions We studied a challenging stage (segmentation) of the process of search interface understanding. In the context of deep Web, this is the third formal empirical study (after [23] and [7]) that groups components belonging to the same logical attribute together. We incorporated the first-hand knowledge of the designer for interface segmentation and component tagging. To the best of our knowledge, this is the first work to apply HMMs on deep Web search interfaces. The interface has been represented in terms of the underlying database. This helped in extracting database querying semantics. Moreover, we tested our method on a less-explored domain (biology), and found promising results. 16
17. Future Work 17 To recover the schema of deep Web databases by extraction of finer details such as data type and constraints of logical attribute. To do justice to the balanced domain distribution of the deep Web [6], we want to test this method on interfaces from other less-explored domains. To improve the degree of automation we want to investigate the use of Baum Welch training algorithm. To minimize the zero emission probabilities, we want to investigate the use of Synset-HMM [20] .
18. References 18 Benslimane, S. M., Malki, M., Rahmouni, M. K., & Benslimane, D. (2007). Extracting personalised ontology from data-intensive web application: An HTML forms-based reverse engineering approach.Informatica, 18(4), 511-534. Freitag, D., & Mccallum, A. K. (1999). Information extraction with HMMs and shrinkage. AAAI-99 Workshop on Machine Learning for Information Extraction, Orlando, Florida. 31-36. Gupta , S., Kaiser, G. E., Grimm , P., Chiang, M. F., & Starren, J. (2005). Automating content extraction of HTML documents. World Wide Web, 8(2), 179-224. Halevy, A. Y. (2005, Why your data won't mix: Semantic heterogeneity. Queue, 3, 50-50-58. He, B., & Chang, K. C. (2003). Statistical schema matching across web query interfaces. 2003 ACM SIGMOD International Conference on Management of Data , San Diego, California. 217-228. He, B., Patel, M., Zhang, Z., & Chang, K. C. (2007a). Accessing the deep web. Communications of the ACM, 50(5), 94-101. He, H., Meng, W., Lu, Y., Yu, C., & Wu, Z. (2007b). Towards deeper understanding of the search interfaces of the deep web. World Wide Web, 10(2), 133 - 155. He, H., Meng, W., Yu, C., & Wu, Z. (2004). Automatic integration of web search interfaces with WISE-integrator. The VLDB Journal the International Journal on very Large Data Bases, 13(3), 256-273. Kalijuvee, O., Buyukkokten, O., Garcia-Molina, H., & Paepcke, A. (2001). Efficient web form entry on PDAs. Proceedings of the 10th International Conference on World Wide Web , Hong Kong, Hong Kong. Kushmerick , N. (2002). Finite-state approaches to web information extraction. 3rd Summer Convention on Information Extraction, 77-91. Kushmerick , N. (2003). Learning to invoke web forms. On the move to meaningful internet systems 2003 (pp. 997-1013) Springer Berlin / Heidelberg. Madhavan, J., Ko, D., Kot, L., Ganapathy, V., Rasmussen, A., & Halevy, A. Y. (2008). Google's deep web crawl. Proceedings of the VLDB Endowment, 1(2), 1241-1252.
19. References 19 Nguyen, H., Nguyen, T., & Freire, J. (2008). Learning to extract form labels. Proceedings of the VLDB Endowment , Auckland, New Zealand. , 1(1) 684-694. Oliver, N., Garg, A., & Horvitz, E. (2004). Layered representations for learning and inferring office activity from multiple sensory channels. Computer Vision and Image Understanding, 96(2), 163-180. Pei, J., Hong, J., & Bell, D. (2006). A robust approach to schema matching over web query interfaces. Proceedings of the 22nd International Conference on Data Engineering Workshops (ICDEW'06), Atlanta, Georgia. 46-55. Rabiner, L., R. (1989). A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2), 257–286. Raghavan, S., & Garcia-Molina, H. (2001). Crawling the hidden web. Proceedings of the 27th International Conference on very Large Data Bases , Rome, Italy. 129-138. Russell, S., J., & Norvig, P. (2002). Artificial intelligence: Modern approach Prentice Hall. Seymore, K., Mccallum, A. K., & Rosenfeld , R. (1999). Learning hidden markov model structure for information extraction. AAAI 99 Workshop on Machine Learning for Information Extraction, Orlando, Florida. 37-42. Tran-Le, M. S., Vo-Dang , T. T., Ho-Van , Q., & Dang, T. K. (2008). Automatic information extraction from the web: An HMM-based approach. Modeling, simulation and optimization of complex processes (pp. 575-585) Springer Berlin Heidelberg. Wang, J., Wen, J., Lochovsky, F., & Ma, W. (2004). Instance-based schema matching for web databases by domain-specific query probing. Thirtieth International Conference on very Large Data Bases, 30, 408 - 419. Wu, W., Yu, C., Doan, A., & Meng, W. (2004). An interactive clustering-based approach to integrating source query interfaces on the deep web. Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data , Paris, France. 95 - 106. Zhang, Z., He, B., & Chang, K. C. (2004). Understanding web query interfaces: Best-effort parsing with hidden syntax. Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data, Paris, France. 107 - 118. Zhong, P., & Chen, J. (2006). A generalized hidden markov model approach for web information extractionWeb Intelligence, 2006. WI 2006, Hong Kong, China. 709-718.