This document discusses database management systems and related concepts. It defines what a database and DBMS are, and describes some common DBMS software. It also explains key database concepts like data types, fields, records, files, and data hierarchy. SQL is introduced as the most common query language for databases. The document outlines the different types of SQL statements for defining, manipulating, and querying data in a database. Database relationships like one-to-one, one-to-many, many-to-one, and many-to-many are also summarized.
DBPEDIA BASED FACTOID QUESTION ANSWERING SYSTEMIJwest
The document describes a factoid question answering system called SELNI that is based on the DBpedia knowledge base. It discusses the system's architecture which involves three main steps: 1) question classification and generating decision models using machine learning, 2) question processing to extract resources and keywords from the question, and 3) formulating and executing SPARQL queries on DBpedia to obtain answers. It also provides details on using support vector machines for question classification and generating models to determine the answer type for a given question. The system aims to answer simple factual questions by utilizing the structured data in DBpedia.
2. an efficient approach for web query preprocessing edit satIAESIJEECS
The emergence of the Web technology generated a massive amount of raw data by enabling Internet users to post their opinions, comments, and reviews on the web. To extract useful information from this raw data can be a very challenging task. Search engines play a critical role in these circumstances. User queries are becoming main issues for the search engines. Therefore a preprocessing operation is essential. In this paper, we present a framework for natural language preprocessing for efficient data retrieval and some of the required processing for effective retrieval such as elongated word handling, stop word removal, stemming, etc. This manuscript starts by building a manually annotated dataset and then takes the reader through the detailed steps of process. Experiments are conducted for special stages of this process to examine the accuracy of the system.
Data Mining for XML Query-Answering SupportIOSR Journals
This document presents a technique for mining tree-based association rules (TARs) from XML documents to support efficient XML query answering. The framework first parses XML documents and mines frequent subtrees. It then extracts TARs from the subtrees and stores them in a new XML file. When queries are received, the stored TARs are used to quickly retrieve relevant information. Experimental results show that query answering time is significantly reduced compared to approaches without mined knowledge. Extraction time increases linearly with the number of nodes in XML documents. The technique provides a useful way to gain information from XML databases in real-time applications.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
Natural Language Processing is a programmed approach to analyze text that is based on both a set of theories and a set of technologies. This forum aims to bring together researchers who have designed and build software that will analyze, understand, and generate languages that humans use naturally to address computers.
HINDI NAMED ENTITY RECOGNITION BY AGGREGATING RULE BASED HEURISTICS AND HIDDE...ijistjournal
Named entity recognition (NER) is one of the applications of Natural Language Processing and is regarded as the subtask of information retrieval. NER is the process to detect Named Entities (NEs) in a document and to categorize them into certain Named entity classes such as the name of organization, person, location, sport, river, city, country, quantity etc. In English, we have accomplished lot of work related to NER. But, at present, still we have not been able to achieve much of the success pertaining to NER in the Indian languages. The following paper discusses about NER, the various approaches of NER, Performance Metrics, the challenges in NER in the Indian languages and finally some of the results that have been achieved by performing NER in Hindi by aggregating approaches such as Rule based heuristics and Hidden Markov Model (HMM).
TALASH: A SEMANTIC AND CONTEXT BASED OPTIMIZED HINDI SEARCH ENGINEIJCSEIT Journal
This document summarizes a research paper that proposes three models for query expansion in a Hindi search engine: 1) Using lexical resources like HindiWordNet to find synonyms and related terms, 2) Using user context information like location, interests and profession, 3) Combining lexical resources and user context. An experiment compares the precision of results from simple Google searches to searches using each model. Precision was highest using the combined Model III at 0.79, showing that integrating lexical and user context information improves search quality in Hindi.
IRJET- Survey for Amazon Fine Food ReviewsIRJET Journal
This document discusses sentiment analysis and summarizes several papers on related topics. It begins with an abstract describing sentiment analysis and its importance. The introduction defines sentiment classification and analysis. The literature survey section summarizes 5 papers on natural language processing and machine learning algorithms for sentiment analysis, including K-means clustering, bag-of-words models, TF-IDF vectorization for document clustering, hierarchical clustering methods, and using naive bayes and SVM for sentiment analysis and text summarization. The conclusion discusses techniques for data processing, natural language processing, and machine learning algorithms covered.
DBPEDIA BASED FACTOID QUESTION ANSWERING SYSTEMIJwest
The document describes a factoid question answering system called SELNI that is based on the DBpedia knowledge base. It discusses the system's architecture which involves three main steps: 1) question classification and generating decision models using machine learning, 2) question processing to extract resources and keywords from the question, and 3) formulating and executing SPARQL queries on DBpedia to obtain answers. It also provides details on using support vector machines for question classification and generating models to determine the answer type for a given question. The system aims to answer simple factual questions by utilizing the structured data in DBpedia.
2. an efficient approach for web query preprocessing edit satIAESIJEECS
The emergence of the Web technology generated a massive amount of raw data by enabling Internet users to post their opinions, comments, and reviews on the web. To extract useful information from this raw data can be a very challenging task. Search engines play a critical role in these circumstances. User queries are becoming main issues for the search engines. Therefore a preprocessing operation is essential. In this paper, we present a framework for natural language preprocessing for efficient data retrieval and some of the required processing for effective retrieval such as elongated word handling, stop word removal, stemming, etc. This manuscript starts by building a manually annotated dataset and then takes the reader through the detailed steps of process. Experiments are conducted for special stages of this process to examine the accuracy of the system.
Data Mining for XML Query-Answering SupportIOSR Journals
This document presents a technique for mining tree-based association rules (TARs) from XML documents to support efficient XML query answering. The framework first parses XML documents and mines frequent subtrees. It then extracts TARs from the subtrees and stores them in a new XML file. When queries are received, the stored TARs are used to quickly retrieve relevant information. Experimental results show that query answering time is significantly reduced compared to approaches without mined knowledge. Extraction time increases linearly with the number of nodes in XML documents. The technique provides a useful way to gain information from XML databases in real-time applications.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
Natural Language Processing is a programmed approach to analyze text that is based on both a set of theories and a set of technologies. This forum aims to bring together researchers who have designed and build software that will analyze, understand, and generate languages that humans use naturally to address computers.
HINDI NAMED ENTITY RECOGNITION BY AGGREGATING RULE BASED HEURISTICS AND HIDDE...ijistjournal
Named entity recognition (NER) is one of the applications of Natural Language Processing and is regarded as the subtask of information retrieval. NER is the process to detect Named Entities (NEs) in a document and to categorize them into certain Named entity classes such as the name of organization, person, location, sport, river, city, country, quantity etc. In English, we have accomplished lot of work related to NER. But, at present, still we have not been able to achieve much of the success pertaining to NER in the Indian languages. The following paper discusses about NER, the various approaches of NER, Performance Metrics, the challenges in NER in the Indian languages and finally some of the results that have been achieved by performing NER in Hindi by aggregating approaches such as Rule based heuristics and Hidden Markov Model (HMM).
TALASH: A SEMANTIC AND CONTEXT BASED OPTIMIZED HINDI SEARCH ENGINEIJCSEIT Journal
This document summarizes a research paper that proposes three models for query expansion in a Hindi search engine: 1) Using lexical resources like HindiWordNet to find synonyms and related terms, 2) Using user context information like location, interests and profession, 3) Combining lexical resources and user context. An experiment compares the precision of results from simple Google searches to searches using each model. Precision was highest using the combined Model III at 0.79, showing that integrating lexical and user context information improves search quality in Hindi.
IRJET- Survey for Amazon Fine Food ReviewsIRJET Journal
This document discusses sentiment analysis and summarizes several papers on related topics. It begins with an abstract describing sentiment analysis and its importance. The introduction defines sentiment classification and analysis. The literature survey section summarizes 5 papers on natural language processing and machine learning algorithms for sentiment analysis, including K-means clustering, bag-of-words models, TF-IDF vectorization for document clustering, hierarchical clustering methods, and using naive bayes and SVM for sentiment analysis and text summarization. The conclusion discusses techniques for data processing, natural language processing, and machine learning algorithms covered.
DUTCH NAMED ENTITY RECOGNITION AND DEIDENTIFICATION METHODS FOR THE HUMAN RES...ijnlc
The human resource (HR) domain contains various types of privacy-sensitive textual data, such as e-mail
correspondence and performance appraisal. Doing research on these documents brings several
challenges, one of them anonymisation. In this paper, we evaluate the current Dutch text de-identification
methods for the HR domain in four steps. First, by updating one of these methods with the latest named
entity recognition (NER) models. The result is that the NER model based on the CoNLL 2002 corpus in
combination with the BERTje transformer give the best combination for suppressing persons (recall 0.94)
and locations (recall 0.82). For suppressing gender, DEDUCE is performing best (recall 0.53). Second
NER evaluation is based on both strict de-identification of entities (a person must be suppressed as a
person) and third evaluation on a loose sense of de-identification (no matter what how a person is
suppressed, as long it is suppressed). In the fourth and last step a new kind of NER dataset is tested for
recognising job titles in tezts.
Intelligent information extraction based on artificial neural networkijfcstjournal
Question Answering System (QAS) is used for information retrieval and natural language processing
(NLP) to reduce human effort. There are numerous QAS based on the user documents present today, but
they all are limited to providing objective answers and process simple questions only. Complex questions
cannot be answered by the existing QAS, as they require interpretation of the current and old data as well
as the question asked by the user. The above limitations can be overcome by using deep cases and neural
network. Hence we propose a modified QAS in which we create a deep artificial neural network with
associative memory from text documents. The modified QAS processes the contents of the text document
provided to it and find the answer to even complex questions in the documents.
Application of hidden markov model in question answering systemsijcsa
By the increase of the volume of the saved information on web, Question Answering (QA) systems have been very important for Information Retrieval (IR). QA systems are a specialized form of information retrieval. Given a collection of documents, a Question Answering system attempts to retrieve correct answers to questions posed in natural language. Web QA system is a sample of QA systems that in this system answers retrieval from web environment doing. In contrast to the databases, the saved information on web does not follow a distinct structure and are not generally defined. Web QA systems is the task of automatically answering a question posed in Natural Language Processing (NLP). NLP techniques are used in applications that make queries to databases, extract information from text, retrieve relevant documents from a collection, translate from one language to another, generate text responses, or recognize spoken words converting them into text. To find the needed information on a mass of the non-structured information we have to use techniques in which the accuracy and retrieval factors are implemented well. In this paper in order to well IR in web environment, The QA system in designed and also implemented based on the Hidden Markov Model (HMM)
Data mining is the knowledge discovery in databases and the gaol is to extract patterns and knowledge from
large amounts of data. The important term in data mining is text mining. Text mining extracts the quality
information highly from text. Statistical pattern learning is used to high quality information. High –quality in
text mining defines the combinations of relevance, novelty and interestingness. Tasks in text mining are text
categorization, text clustering, entity extraction and sentiment analysis. Applications of natural language
processing and analytical methods are highly preferred to turn
Dynamic Construction of Telugu Speech Corpus for Voice Enabled Text EditorWaqas Tariq
In recent decades speech interactive systems have gained increasing importance. Performance of an ASR system mainly depends on the availability of large corpus of speech. The conventional method of building a large vocabulary speech recognizer for any language uses a top-down approach to speech. This approach requires large speech corpus with sentence or phoneme level transcription of the speech utterances. The transcriptions must also include different speech order so that the recognizer can build models for all the sounds present. But, for Telugu language, because of its complex nature, a very large, well annotated speech database is very difficult to build. It is very difficult, if not impossible, to cover all the words of any Indian language, where each word may have thousands and millions of word forms. A significant part of grammar that is handled by syntax in English (and other similar languages) is handled within morphology in Telugu. Phrases including several words (that is, tokens) in English would be mapped on to a single word in Telugu.Telugu language is phonetic in nature in addition to rich in morphology. That is why the speech technology developed for English cannot be applied to Telugu language. This paper highlights the work carried out in an attempt to build a voice enabled text editor with capability of automatic term suggestion. Main claim of the paper is the recognition enhancement process developed by us for suitability of highly inflecting, rich morphological languages. This method results in increased speech recognition accuracy with very much reduction in corpus size. It also adapts Telugu words to the database dynamically, resulting in growth of the corpus.
Answer extraction and passage retrieval forWaheeb Ahmed
—Question Answering systems (QASs) do the task of
retrieving text portions from a collection of documents that
contain the answer to the user’s questions. These QASs use a
variety of linguistic tools that be able to deal with small
fragments of text. Therefore, to retrieve the documents which
contains the answer from a large document collections, QASs
employ Information Retrieval (IR) techniques to minimize the
number of documents collections to a treatable amount of
relevant text. In this paper, we propose a model for passage
retrieval model that do this task with a better performance for
the purpose of Arabic QASs. We first segment each the top five
ranked documents returned by the IR module into passages.
Then, we compute the similarity score between the user’s
question terms and each passage. The top five passages (with
high similarity score) are retrieved are retrieved. Finally,
Answer Extraction techniques are applied to extract the final
answer. Our method achieved an average for precision of
87.25%, Recall of 86.2% and F1-measure of 87%.
Architecture of an ontology based domain-specific natural language question a...IJwest
The document summarizes the architecture of an ontology-based domain-specific natural language question answering system. The proposed architecture defines four main modules: 1) question processing which analyzes and classifies questions and reformulates queries, 2) document retrieval which retrieves relevant documents, 3) document processing which processes retrieved documents, and 4) answer extraction which extracts and generates responses. Natural language processing techniques and ontologies are used to analyze questions and documents and extract relationships and answers. The system aims to generate concise, specific answers to natural language questions in a given domain and achieved 94% accuracy in testing.
A Review on Grammar-Based Fuzzing TechniquesCSCJournals
Fuzzing has become the most interesting software testing technique because it can find different types of bugs and vulnerabilities in many target programs. Grammar-based fuzzing tools have been shown effectiveness in finding bugs and generating good fuzzing files. Fuzzing techniques are usually guided by different methods to improve their effectiveness. However, they have limitation as well. In this paper, we present an overview of grammar-based fuzzing tools and techniques that are used to guide them which include mutation, machine learning, and evolutionary computing. Few studies are conducted on this approach and show the effectiveness and quality in exploring new vulnerabilities in a program. Here we summarize the studied fuzzing tools and explain each one method, input format, strengths and limitations. Some experiments are conducted on two of the fuzzing tools and comparing between them based on the quality of generated fuzzing files.
Survey on Existing Text Mining Frameworks and A Proposed Idealistic Framework...ijceronline
International 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.
The document discusses a framework for web information retrieval using automatic multi-document summarization. It proposes using multi-level document summarization to enhance the effectiveness of web information retrieval by supporting indexing and ranking of retrieved documents with an intelligent decision making system based on fuzzy inference rules. The paper tests the approach on CACM test data and finds that information retrieval results can be improved after performing a multi-document summarization process.
A Simple Information Retrieval Techniqueidescitation
The document presents a simple information retrieval technique that involves removing stop words and punctuation from documents, calculating term frequency and inverse document frequency, constructing a master document matrix, and ranking documents based on similarity to user queries. The technique is demonstrated on a sample collection of 5 documents. For a query on "information retrieval system", the documents are ranked from most similar to least similar as document 5, document 1, document 2, document 3, document 4. The technique provides an easy way to search and retrieve relevant documents from a collection.
This document describes a method for enriching search results using ontology. It begins with an abstract discussing how keyword searches often return irrelevant documents due to the large amount of information available online. It then introduces the concept of using ontology to allow for more sophisticated semantic searches. The paper presents an architecture that augments keyword search results with additional documents that are semantically relevant based on ontology mappings. Documents in the search results are then ranked based on both keyword frequency and semantic relevance to improve search accuracy.
DBMS UNIT_1: Introduction Application of DMBS,
Advantages & Disadvantages.
Internal Level/Schema
Conceptual Level/Schema
Physical Level/Schema
Logical Data Independence
Physical Data Independence
Role of DBA
Type of Database user
The document provides an introduction to databases and database management systems. It discusses key concepts such as data, information, database files, tables, fields, records, primary keys, and foreign keys. It also describes different types of databases and applications of database management systems. Finally, it covers topics like planning a database, database users, security, and integrity.
This document provides an outline for a course on electronic data processing and databases. It covers topics such as managing files, database management systems, database models like relational and hierarchical, features of DBMS including data dictionaries and security. It also discusses databases and the new economy including e-commerce and data mining. The last section discusses ethics around manipulating media and ensuring accuracy and privacy of databases.
This document provides an introduction and overview for a database systems course. It outlines the course details including classes, instructor information, textbook recommendations, and grading policy. The course will cover topics such as data models, database programming, and database management systems. It will also discuss the history and evolution of databases as well as the components, functions, and trends of database management systems.
This document provides an introduction to SQL and databases. It discusses the proliferation of data and importance of databases. Key topics covered include different types of databases, the components of a database system including the DBMS, and the functions of a DBMS. The document traces the evolution of databases from manual file systems to integrated database management systems and discusses important database terminology like metadata and relationships. It also emphasizes the importance of database design.
Here is a 500 word essay on social network analysis:
[Introduction]
Social network analysis (SNA) is an approach for analyzing the relationships and flows between people, groups, organizations, computers or other connected information/knowledge processing entities. SNA provides both a visual and mathematical analysis of human relationships that aid in understanding complex social structures. By assessing relationship patterns, SNA can help reveal how relationships cluster and help or hinder the flow of information, influence the diffusion of ideas, and impact knowledge sharing.
[Body paragraph 1]
A fundamental concept in SNA is that relationships rather than individual actors should be the primary focus of analysis. SNA represents these relationships in network graphs where nodes are the individual actors and ties represent
The document discusses database management systems (DBMS). It defines key concepts like data, databases, and DBMS. It explains that a DBMS is software that manages databases and makes data storage and retrieval easier. The document also covers database models like relational, network and hierarchical, different types of DBMS languages, purposes of DBMS, advantages and disadvantages. It provides examples of database usage in domains like banking, airlines, universities etc.
This document provides an introduction to database management systems (DBMS). It discusses key concepts such as database models including hierarchical, network, relational and entity-relationship models. It also covers database planning, design, implementation and maintenance. Specific topics covered include data modeling, database normalization, query languages, transaction management and database administration.
Database management systems (DBMS) help organize data across departments to provide timely, accurate information for better decision-making. A DBMS includes database software, users, and practitioners who design database structures and applications. It defines data through a data dictionary for clear understanding and prevents errors. A DBMS also secures data and maintains integrity through backup and recovery.
This document provides information about database management systems (DBMS). It defines a DBMS as a software system that stores data, processes data, and provides information in an organized way. It discusses some popular DBMS software like MS Access, Oracle, SQL Server, and MySQL. The document also explains some key concepts in DBMS like tables, records, fields, and objects. It provides examples of how a database with tables can be used to store and organize information.
DUTCH NAMED ENTITY RECOGNITION AND DEIDENTIFICATION METHODS FOR THE HUMAN RES...ijnlc
The human resource (HR) domain contains various types of privacy-sensitive textual data, such as e-mail
correspondence and performance appraisal. Doing research on these documents brings several
challenges, one of them anonymisation. In this paper, we evaluate the current Dutch text de-identification
methods for the HR domain in four steps. First, by updating one of these methods with the latest named
entity recognition (NER) models. The result is that the NER model based on the CoNLL 2002 corpus in
combination with the BERTje transformer give the best combination for suppressing persons (recall 0.94)
and locations (recall 0.82). For suppressing gender, DEDUCE is performing best (recall 0.53). Second
NER evaluation is based on both strict de-identification of entities (a person must be suppressed as a
person) and third evaluation on a loose sense of de-identification (no matter what how a person is
suppressed, as long it is suppressed). In the fourth and last step a new kind of NER dataset is tested for
recognising job titles in tezts.
Intelligent information extraction based on artificial neural networkijfcstjournal
Question Answering System (QAS) is used for information retrieval and natural language processing
(NLP) to reduce human effort. There are numerous QAS based on the user documents present today, but
they all are limited to providing objective answers and process simple questions only. Complex questions
cannot be answered by the existing QAS, as they require interpretation of the current and old data as well
as the question asked by the user. The above limitations can be overcome by using deep cases and neural
network. Hence we propose a modified QAS in which we create a deep artificial neural network with
associative memory from text documents. The modified QAS processes the contents of the text document
provided to it and find the answer to even complex questions in the documents.
Application of hidden markov model in question answering systemsijcsa
By the increase of the volume of the saved information on web, Question Answering (QA) systems have been very important for Information Retrieval (IR). QA systems are a specialized form of information retrieval. Given a collection of documents, a Question Answering system attempts to retrieve correct answers to questions posed in natural language. Web QA system is a sample of QA systems that in this system answers retrieval from web environment doing. In contrast to the databases, the saved information on web does not follow a distinct structure and are not generally defined. Web QA systems is the task of automatically answering a question posed in Natural Language Processing (NLP). NLP techniques are used in applications that make queries to databases, extract information from text, retrieve relevant documents from a collection, translate from one language to another, generate text responses, or recognize spoken words converting them into text. To find the needed information on a mass of the non-structured information we have to use techniques in which the accuracy and retrieval factors are implemented well. In this paper in order to well IR in web environment, The QA system in designed and also implemented based on the Hidden Markov Model (HMM)
Data mining is the knowledge discovery in databases and the gaol is to extract patterns and knowledge from
large amounts of data. The important term in data mining is text mining. Text mining extracts the quality
information highly from text. Statistical pattern learning is used to high quality information. High –quality in
text mining defines the combinations of relevance, novelty and interestingness. Tasks in text mining are text
categorization, text clustering, entity extraction and sentiment analysis. Applications of natural language
processing and analytical methods are highly preferred to turn
Dynamic Construction of Telugu Speech Corpus for Voice Enabled Text EditorWaqas Tariq
In recent decades speech interactive systems have gained increasing importance. Performance of an ASR system mainly depends on the availability of large corpus of speech. The conventional method of building a large vocabulary speech recognizer for any language uses a top-down approach to speech. This approach requires large speech corpus with sentence or phoneme level transcription of the speech utterances. The transcriptions must also include different speech order so that the recognizer can build models for all the sounds present. But, for Telugu language, because of its complex nature, a very large, well annotated speech database is very difficult to build. It is very difficult, if not impossible, to cover all the words of any Indian language, where each word may have thousands and millions of word forms. A significant part of grammar that is handled by syntax in English (and other similar languages) is handled within morphology in Telugu. Phrases including several words (that is, tokens) in English would be mapped on to a single word in Telugu.Telugu language is phonetic in nature in addition to rich in morphology. That is why the speech technology developed for English cannot be applied to Telugu language. This paper highlights the work carried out in an attempt to build a voice enabled text editor with capability of automatic term suggestion. Main claim of the paper is the recognition enhancement process developed by us for suitability of highly inflecting, rich morphological languages. This method results in increased speech recognition accuracy with very much reduction in corpus size. It also adapts Telugu words to the database dynamically, resulting in growth of the corpus.
Answer extraction and passage retrieval forWaheeb Ahmed
—Question Answering systems (QASs) do the task of
retrieving text portions from a collection of documents that
contain the answer to the user’s questions. These QASs use a
variety of linguistic tools that be able to deal with small
fragments of text. Therefore, to retrieve the documents which
contains the answer from a large document collections, QASs
employ Information Retrieval (IR) techniques to minimize the
number of documents collections to a treatable amount of
relevant text. In this paper, we propose a model for passage
retrieval model that do this task with a better performance for
the purpose of Arabic QASs. We first segment each the top five
ranked documents returned by the IR module into passages.
Then, we compute the similarity score between the user’s
question terms and each passage. The top five passages (with
high similarity score) are retrieved are retrieved. Finally,
Answer Extraction techniques are applied to extract the final
answer. Our method achieved an average for precision of
87.25%, Recall of 86.2% and F1-measure of 87%.
Architecture of an ontology based domain-specific natural language question a...IJwest
The document summarizes the architecture of an ontology-based domain-specific natural language question answering system. The proposed architecture defines four main modules: 1) question processing which analyzes and classifies questions and reformulates queries, 2) document retrieval which retrieves relevant documents, 3) document processing which processes retrieved documents, and 4) answer extraction which extracts and generates responses. Natural language processing techniques and ontologies are used to analyze questions and documents and extract relationships and answers. The system aims to generate concise, specific answers to natural language questions in a given domain and achieved 94% accuracy in testing.
A Review on Grammar-Based Fuzzing TechniquesCSCJournals
Fuzzing has become the most interesting software testing technique because it can find different types of bugs and vulnerabilities in many target programs. Grammar-based fuzzing tools have been shown effectiveness in finding bugs and generating good fuzzing files. Fuzzing techniques are usually guided by different methods to improve their effectiveness. However, they have limitation as well. In this paper, we present an overview of grammar-based fuzzing tools and techniques that are used to guide them which include mutation, machine learning, and evolutionary computing. Few studies are conducted on this approach and show the effectiveness and quality in exploring new vulnerabilities in a program. Here we summarize the studied fuzzing tools and explain each one method, input format, strengths and limitations. Some experiments are conducted on two of the fuzzing tools and comparing between them based on the quality of generated fuzzing files.
Survey on Existing Text Mining Frameworks and A Proposed Idealistic Framework...ijceronline
International 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.
The document discusses a framework for web information retrieval using automatic multi-document summarization. It proposes using multi-level document summarization to enhance the effectiveness of web information retrieval by supporting indexing and ranking of retrieved documents with an intelligent decision making system based on fuzzy inference rules. The paper tests the approach on CACM test data and finds that information retrieval results can be improved after performing a multi-document summarization process.
A Simple Information Retrieval Techniqueidescitation
The document presents a simple information retrieval technique that involves removing stop words and punctuation from documents, calculating term frequency and inverse document frequency, constructing a master document matrix, and ranking documents based on similarity to user queries. The technique is demonstrated on a sample collection of 5 documents. For a query on "information retrieval system", the documents are ranked from most similar to least similar as document 5, document 1, document 2, document 3, document 4. The technique provides an easy way to search and retrieve relevant documents from a collection.
This document describes a method for enriching search results using ontology. It begins with an abstract discussing how keyword searches often return irrelevant documents due to the large amount of information available online. It then introduces the concept of using ontology to allow for more sophisticated semantic searches. The paper presents an architecture that augments keyword search results with additional documents that are semantically relevant based on ontology mappings. Documents in the search results are then ranked based on both keyword frequency and semantic relevance to improve search accuracy.
DBMS UNIT_1: Introduction Application of DMBS,
Advantages & Disadvantages.
Internal Level/Schema
Conceptual Level/Schema
Physical Level/Schema
Logical Data Independence
Physical Data Independence
Role of DBA
Type of Database user
The document provides an introduction to databases and database management systems. It discusses key concepts such as data, information, database files, tables, fields, records, primary keys, and foreign keys. It also describes different types of databases and applications of database management systems. Finally, it covers topics like planning a database, database users, security, and integrity.
This document provides an outline for a course on electronic data processing and databases. It covers topics such as managing files, database management systems, database models like relational and hierarchical, features of DBMS including data dictionaries and security. It also discusses databases and the new economy including e-commerce and data mining. The last section discusses ethics around manipulating media and ensuring accuracy and privacy of databases.
This document provides an introduction and overview for a database systems course. It outlines the course details including classes, instructor information, textbook recommendations, and grading policy. The course will cover topics such as data models, database programming, and database management systems. It will also discuss the history and evolution of databases as well as the components, functions, and trends of database management systems.
This document provides an introduction to SQL and databases. It discusses the proliferation of data and importance of databases. Key topics covered include different types of databases, the components of a database system including the DBMS, and the functions of a DBMS. The document traces the evolution of databases from manual file systems to integrated database management systems and discusses important database terminology like metadata and relationships. It also emphasizes the importance of database design.
Here is a 500 word essay on social network analysis:
[Introduction]
Social network analysis (SNA) is an approach for analyzing the relationships and flows between people, groups, organizations, computers or other connected information/knowledge processing entities. SNA provides both a visual and mathematical analysis of human relationships that aid in understanding complex social structures. By assessing relationship patterns, SNA can help reveal how relationships cluster and help or hinder the flow of information, influence the diffusion of ideas, and impact knowledge sharing.
[Body paragraph 1]
A fundamental concept in SNA is that relationships rather than individual actors should be the primary focus of analysis. SNA represents these relationships in network graphs where nodes are the individual actors and ties represent
The document discusses database management systems (DBMS). It defines key concepts like data, databases, and DBMS. It explains that a DBMS is software that manages databases and makes data storage and retrieval easier. The document also covers database models like relational, network and hierarchical, different types of DBMS languages, purposes of DBMS, advantages and disadvantages. It provides examples of database usage in domains like banking, airlines, universities etc.
This document provides an introduction to database management systems (DBMS). It discusses key concepts such as database models including hierarchical, network, relational and entity-relationship models. It also covers database planning, design, implementation and maintenance. Specific topics covered include data modeling, database normalization, query languages, transaction management and database administration.
Database management systems (DBMS) help organize data across departments to provide timely, accurate information for better decision-making. A DBMS includes database software, users, and practitioners who design database structures and applications. It defines data through a data dictionary for clear understanding and prevents errors. A DBMS also secures data and maintains integrity through backup and recovery.
This document provides information about database management systems (DBMS). It defines a DBMS as a software system that stores data, processes data, and provides information in an organized way. It discusses some popular DBMS software like MS Access, Oracle, SQL Server, and MySQL. The document also explains some key concepts in DBMS like tables, records, fields, and objects. It provides examples of how a database with tables can be used to store and organize information.
The document discusses a workshop on designing information systems for business organizations. It covers topics like the $10 billion industry shift towards information management, motivation for next generation databases, challenges of database technology, scenarios involving instant virtual enterprises and personalized information systems, and the aims and objectives of familiarizing participants with database development techniques.
Database concepts presentation version 2010 revisedmnodalo
This document provides an overview of key database concepts including:
- The difference between data and information
- Flat file and relational databases
- Database structure including tables, records, and fields
- Key aspects of database design such as field names, data types, lengths, and descriptions
- Functions of database management systems (DBMS) like Microsoft Access to create, edit, search, and report on database contents
- The purpose and components of structured query language (SQL) to filter and extract specific records from a database
The objective is for students to understand these fundamental database terminologies and concepts.
This document provides an introduction to database concepts including definitions of data, information, and databases. It discusses the data processing cycle and differences between manual and computerized data processing. It also describes database users like system analysts, application programmers, and end users. Additionally, it covers database features such as redundancy control, data integrity, data sharing, and security. It discusses data abstraction, database models including hierarchical, network and relational models, as well as normalization. Other topics include database architecture, physical and logical data independence, and entity-relationship diagrams.
This presentation discusses the importance of data architecture and database security. It emphasizes creating precise data structures when handling large amounts of data, as companies rely on manipulating data. Normalizing data structures improves performance, maintenance and flexibility. Examples show how normalizing a persons table reduces data size by 97% and speeds up queries by 40-150x. The speaker recommends best practices for data structure, security and limiting direct data access to improve protection.
The document provides an overview of database management systems (DBMS). It discusses that a DBMS contains organized data about an enterprise. It offers advantages over file systems like avoiding data redundancy and inconsistencies. The document describes database applications, levels of abstraction in a DBMS, the relational data model using tables and SQL, and components of the database engine like storage management, query processing, and transaction management. It also provides a brief history of database systems from the 1950s to modern times.
This document introduces key concepts related to database systems including:
1. Data is raw facts that are organized into meaningful information by computers. Data integrity ensures data is correct to create accurate information.
2. A database contains files with records made of fields that store characters of data. Common data types include text, numbers, dates. Validation checks help ensure data integrity.
3. A database management system (DBMS) allows users to add, retrieve, and manage shared data across programs more easily compared to file systems. It addresses issues like data redundancy, inconsistent data, and concurrent access.
This document provides an overview of key concepts in database management systems including:
1. It discusses different data models including relational, entity-relationship, and object-oriented models.
2. It describes database system components like data definition language, data manipulation language, and transaction management.
3. It outlines different types of users that interact with database systems and roles like database administrators.
This document provides an overview of fundamentals of database design. It discusses what a database is, the difference between data and information, why databases are needed, how to select a database system, basic database definitions and building blocks, quality control considerations, and data entry methods. The overall purpose of a database management system is to transform data into information, information into knowledge, and knowledge into action.
This document provides an overview of fundamentals of database design. It discusses what a database is, the difference between data and information, why databases are needed, how to select a database system, basic database definitions and building blocks, quality control considerations, and data entry methods. The overall purpose of a database management system is to transform data into information, information into knowledge, and knowledge into action.
Similar to DBMS Class Presentation for English Version. (20)
This document contains information about Md. Ikbol Hossain including his educational and professional background. It lists his current position as Lecturer (ICT) at Adamjee Cantonment College and previous position as Ex-Lecturer (ICT) at BAF Shaheen College Dhaka. It also provides his contact information and summaries some theorems and proofs related to Boolean algebra.
The document discusses virtual reality, defined as technology that allows users to interact with computer-generated worlds through sight, sound, and touch. It provides examples of how virtual reality can be used in education by allowing students to experience different environments. The document also outlines potential benefits of virtual reality, such as for job training, exposure therapy, and entertainment, as well as some challenges like cyber sickness and privacy issues. In conclusion, it emphasizes the importance of virtual reality for future development.
The document discusses the global village concept, providing definitions from various sources. It describes how the term was coined by Herbert Marshall McLuhan and outlines some key advantages of living in a global village, such as access to information from around the world, communication across borders, increased knowledge and skills, and opportunities for education, healthcare and entertainment globally.
The document discusses wireless communication systems and technologies like Bluetooth, Wi-Fi, and WiMAX. It provides an overview of the types of wireless networks including wireless personal area networks (WPAN), wireless local area networks (WLAN), wireless metropolitan area networks (WMAN), and wireless wide area networks (WWAN). It describes some key features and applications of Bluetooth including connecting devices within 10 meters of each other. It also outlines characteristics and uses of Wi-Fi for connecting devices to the internet wirelessly within local areas. Finally, it introduces WiMAX as a wireless technology that can provide broadband internet access over long distances of up to 60 km.
The document discusses different types of data communication mediums. It describes wired mediums like coaxial cable, twisted-pair cable, and fiber optic cable. It provides details on the characteristics, advantages, and disadvantages of each. Coaxial cable can support speeds up to 200 Mbps but has a shorter transmission distance compared to other options. Twisted-pair cable is commonly used for telephone lines and network cables in buildings. Fiber optic cable is capable of speeds over 1 Gbps and has several advantages for long-distance, high-speed data transmission.
This document provides an overview of computer networking by MD IKBAL HOSSAIN, a lecturer at ADAMJEE CANTONMENT COLLEGE. It discusses the definition, uses, and challenges of computer networking. It also describes the types of computer networks including personal area networks, local area networks, metropolitan area networks, and wide area networks. Additionally, it covers the necessary hardware for building computer networks such as network interface cards and modems.
This document provides an overview of mobile communications and cell phone technology. It discusses key aspects of GSM and CDMA cellular networks. Specific topics covered include the structure of SIM cards in GSM networks, the different types of cellular phones, and features of mobile communications such as calling, texting, and internet access. The document also outlines advantages and limitations of the GSM standard for cellular networks.
1. The document discusses different types of communication systems and data transmission methods.
2. Communication systems can be categorized based on the type of communication and participants in the communication. Common types include wired, wireless, broadcast, interactive, etc.
3. Data transmission methods include asynchronous transmission where data is transmitted without timing signals, synchronous transmission where data is transmitted based on a timing signal, and isochronous transmission for real-time data like audio and video.
MD Ikbol Hossain is a lecturer of ICT at Adamjee Cantonment College. He previously worked as an Ex-Lecturer of ICT at BAF Shaheen College Dhaka and BAF Shaheen College Shamshernagar. He has a B.Sc and M.Sc degree in Applied Physics and Electronic Engineering from Rajshahi University. The document provides his contact information and guidelines on how to be a good student and teacher.
This document contains the resume and contact information of MD. IKBAL HOSSAIN, who is a LECTURER (ICT) at ADAMJEE CANTONMENT COLLEGE. It lists his educational qualifications including a B.Sc and M.Sc in Applied Physics and Electronic Engineering from Rajshahi University. It also provides his cell phone number and email address for contact. The document then lists some guidelines and tips for students, including studying regularly, focusing on important topics, maintaining a positive mindset, and preparing well for exams.
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
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This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
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There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
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2. Database Management System
Md. Ikbal Hossain,Lecturer(ICT),Adamjee Cantonment College
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Primary Concept of Database
1. What is Data?
The word data is the plural number of latin
word Datum.
The unordered events that’s are used for
processing are called data.
Data is the formation of character, number &
symbol.
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Primary Concept of Database
Types Of data
There are mainly three types of Data
1. Numeric
2. Non Numeric
3. Boolean data
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Primary Concept of Database
Types Of data
Data
BooleanNumeric
ObjectStringCharacter
FalseTrue
Floating PointInteger
Non-Numeric
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Information
Information is the orderly and useful arrangement of
data so that they are accurate,timely,complete and
concise.
Primary Concept of Database
Roll Name Section
01 Rahim Electron
02 Moli Proton
03 Kamal Neutron
Information
Data
Data
Data
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Differences Between data and Information
Data Information
1.Data is the Single unit of
Information.
1.Information is orderly and
useful arrangement of Data
2. Data is unprocessed facts
figures.
2. Information is processed data.
3. Data doesn’t depend on
Information.
3. Information depends on data.
4. Data is not specific. 4. Information is specific.
5. Data doesn’t carry a meaning. 5. Information must carry a
logical meaning.
6. Data is the raw material. 6. Information is the product.
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Primary Concept of Database
Data Hierarchy
Data hierarchy refers to the systematic
organization of data, often in a hierarchical form.
Data organization involves bit, characters, fields,
records, files and so on.
9. Database Management System
Md. Ikbal Hossain,Lecturer(ICT),Adamjee Cantonment College
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What is database?
A Database is a collection of Related data.
Advantages of Database
• Reduced data redundancy
• Reduced updating errors and increased consistency
• Greater data integrity and independence from applications
programs
• Improved data access to users through use of host and query
languages
• Improved data security
• Reduced data entry, storage, and retrieval costs
• Facilitated development of new applications program
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Md. Ikbal Hossain,Lecturer(ICT),Adamjee Cantonment College
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What is DBMS?
• A database management system (DBMS) is
system software for creating and managing
databases.
• A Database Management System(DBMS) is a
set of computer programs that controlls the
creation,maintenance, and the use of Database.
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Software of DBMS
Oracle
dBASE
FoxPro
MS Office Access
My SQL
SQLite
FileMaker
Firebird. …………..etc
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Fundamental Function of DBMS
• Create new Database
• Include Record in Database
• Update Record
• Delete wrong or redundant Data/Record.
• Data searching.
• Data Query.
• Data sorting.
• Data Indexing.
• Data Dictionary Management.
• Data Storage Management.
• Security Management.
• Multi User Access Control,
• Backup and Recovery Management,
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Application and Uses of Database
Management System (DBMS)
• Education sector:
• Banking System:
• Industry:
• Telecommunications
• Railway Airlines Reservation System
• Hospital management
• Library Management System
• Human Resource Management
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Md. Ikbal Hossain,Lecturer(ICT),Adamjee Cantonment College
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Field Type /Data Type in Database.
There are 10 types of Field or Data in Database.
1. Text/Character
2. Auto Number
3. Number/Numeric
4. Logical/Yes or No
5. Date/Time
6. Memo
7. Currency
8. Object linking and embedding
9. Hyperlink
10. Lookup Wizard
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Md. Ikbal Hossain,Lecturer(ICT),Adamjee Cantonment College
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Key Field
Question: What is Key Field? Discuss various
types of Key Field.
Ans:
The field which is used for identifying the record of file
and database.
Three types of Key Field
1. Primary Key Field.
2. Composite Primary Key Field.
3. Foreign Key Field.
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(Primary Key Field).
What is Primary Key Field?
A field in a record that holds unique data which identifies that record from
all the other records in the file or database.
Ex: Student id ,Account number, product code are typical key fields
Student Id Name GPA
1001 Rita 5.00
1002 Mita 4.75
1003 Rita 4.50
1004 Nita 5.00
Primary Key Field
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Composite Primary Key Field
A composite key is a set of more than one key that,
together, uniquely identifies each record.
Roll Class GPA
1001 Seven 4.50
1004 Eight 4.80
1005 Nine 5.00
1001 Ten 4.75
1002 Eleven 4.00
Composite Primary Key Field
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Foreign Key Field.
If a primary key field of one table is used as normal field of
another table then the field is called Foreign Key Field.
Roll St_Name SSC GPA
1001 Poli 5.00
1002 Moli 4.50
1003 Doli 4.75
Reg_No Roll HSC GPA
5225 1001 5.00
5227 1002 5.00
5228 1003 5.00
Student Admission Table Result Table
Primary key Foreign key
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RDBMS
What is RDBMS ?
The Full name of RDBMS is Relational Database
Management System.
A relational database management system (RDBMS)
is a program that lets a user to create, update, and
administer a relational database.
In 1970 Edgar F. Codd, Invented RDBMS.
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Md. Ikbal Hossain,Lecturer(ICT),Adamjee Cantonment College
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Query
What is Query ?
The process of retrieving data from database according to
condition is called Query .
A database Query is a piece of code constructed using SQL(A
high level Programming Language).
Classification of Query:
1. Select Query
2. Parameter Query
3. Crosstab Query
4. Unmatched Query
5. Action Query
Append Query
Update Query
Delete Query
Make table Query
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Query Language
What is Query Language?
The language which is used to insert data,Modify data,update
data,maintain data,delete date,retrieves data from Database is
called Query Language.
According to Data manipulation Query Language are
classified as…
1.QUEL(Query Language)
2.QBE(Query By Example)
3.SQL(Structured Query language)
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SQL(Structured Query language)
The commands of SQL are written by two methods.
1. DDL(Data Definition Language)
2. DML(Data Manipulation Language)
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DDL(Data Definition Language)
What is DDL ?
Data Definition Language (DDL) is a standard for commands
that define the different structures in a database. DDL
statements create, modify, and remove database objects such
as tables, indexes, and users.
Common DDL statements are
CREATE, RENAME,ALTER, and DROP
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SQL Statement by using DDL.
Create a new table by using SQL CREATE statement.
CREATE TABLE student_Informations
(
ID NO Number Primary Key,
Name text(20),
Father_Name text(20),
Mother_Name text(20),
Group text(20),
Tution_Fees Currency,
Class_start_date date
);
IDno Name Father_Name Mother_Name Group Tution_Fees Class_Start_Date
25. Database Management System
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SQL Statement by using DDL.
Rename a table by using SQL RENAME
statement.
Syntax of RENAME COMMAND:
RENAME old table name TO new table name;
EX:
RENAME student_Informations To students;
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SQL Statement by using DDL.
Update a table by using SQL statement.
1. Add a new field in a Table .
Syntax to add a new field in a database table :
ALTER TABLE table name
ADD (newcolumname1 datatype(size), newcolumname2 datatype(size));
EX:
ALTER TABLE student_Informations
ADD GPA number(10);
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SQL Statement by using DDL.
Update a table by using SQL statement.
2. Change Field Type and Field Size of a database Table.
Syntax:
ALTER TABLE table name
MODIFY (columname newdatatype(newsize));
EX:
ALTER TABLE student_Informations
MODIFY GPA text(5);
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SQL Statement by using DDL.
Update a table by using SQL statement.
3. Delete a Field from a database Table
Syntax:
ALTER TABLE table name
DROP columname;
EX:
ALTER TABLE student_Informations
DROP GPA;
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SQL Statement by using DDL.
Update a table by using SQL statement.
4. Delete a Table From database
Syntax:
DROP TABLE table name
EX:
DROP TABLE student_Informations
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SQL Statement by using DML.
Statements used in DML
1. INSERT
2. SELECT
3. UPDATE
4. DELETE
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SQL Statement by using DML.
INSERT Statement
1.Insert a new Record in database table by using SQL INSERT Statement
Syntax:
INSERT INTO table name
(Columnname1, Columnname1, Columnname1……)
VALUES(Value1,Value2,Value3,…………);
Ex:
INSERT INTO student_Information
(ID_no,Name,Father_Name,Mother_Name,Group,Tution_Fees,Class_start_date date)
VALUES (1003,“Razu”,“Razzak”,“sofia”,“Science”,2500,01/07/2017);
ID_no Name Father_Name Mother_Name Group Tution_Fees Class_Start_Date
1003 Razu Razzak Sofia Science 2500 01/07/2017
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SQL Statement by using DML.
SELECT Statements for viewing all fields of a Table.
Syntax:
SELECT *
From table name;
EX:
SELECT *
FROM student_Informations;
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SQL Statement by using DML.
SELECT Statements for viewing specific field of a Table.
Syntax:
SELECT column1, column2, column3, ……
From table name;
EX:
SELECT ID_no, Name
FROM student_Informations;
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SQL Statement by using DML.
SELECT Statements for viewing specific field of a Table.
Syntax:
SELECT column1, column2, column3, ……
From table name
Where columnname=value;
EX:
SELECT ID no, Name
FROM student_Informations
WHERE section=“science”;
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SQL Statement by using DML.
SELECT Statements for viewing all field of a Table and sorting the
record by descending order.
Syntax:
SELTCT *
From table name
Where columnname=value;
EX:
SELTCT *
FROM student_Informations
WHERE section=“science”
ORDERED BY Name DESC;
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SQL Statement by using DML.
SELECT Statements for viewing all field of a Table and sorting the
record by aescending order.
Syntax:
SELTCT *
From table name
Where columnname=value;
EX:
SELTCT *
FROM student_Informations
WHERE ((section=“science”) AND (GPA>=4.5))
ORDERED BY Name ASC;
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SQL Statement by using DML.
UPDATE statement
Syntax:
UPDATE table name
SET (columnname=value…………;
WHERE Condition;
EX:
UPDATE student_Informations
SET Name=“Raza”
WHERE ID no=1003;
38. Database Management System
Md. Ikbal Hossain,Lecturer(ICT),Adamjee Cantonment College
P-38
SQL Statement by using DML.
DELETE statement
Delete All Record From a table.
Syntax:
DELETE FROM table name
EX:
DELETE FROM student_Informations
OR
TRUNCATE TABLE student_Informations
39. Database Management System
Md. Ikbal Hossain,Lecturer(ICT),Adamjee Cantonment College
P-39
SQL Statement by using DML.
DELETE statement
Delete specific Record from Table
Syntax:
DELETE FROM table name
WHERE Condition;
EX:
DELETE FROM student_Informations
WHERE ID no=1003;
40. Database Management System
Md. Ikbal Hossain,Lecturer(ICT),Adamjee Cantonment College
P-40
Database Relation
The logical relationship with various database table
in a Database is called database relation.
4 types of Database Relation:
1. One to One.
2. One to Many
3. Many to One
4. Many to Many.
41. Database Management System
Md. Ikbal Hossain,Lecturer(ICT),Adamjee Cantonment College
P-41
One to one Relation
The relation with one record of a table to another one
record of another table is called one to one relation.
This relation is possible only between two primary
key.
43. Database Management System
Md. Ikbal Hossain,Lecturer(ICT),Adamjee Cantonment College
P-43
One to Many
The relation with one record of a table to many record
of another table is called one to many relation.
This relation is possible only between a primary key
and with a foreign key.
45. Database Management System
Md. Ikbal Hossain,Lecturer(ICT),Adamjee Cantonment College
P-45
Many to One Relation.
The relation between many record of a table to only
with one record of another table is called many to
one relation.
This relation is possible only between a foreign key
with a primary key.
47. Database Management System
Md. Ikbal Hossain,Lecturer(ICT),Adamjee Cantonment College
P-47
• Many to Many Relation
The relation with many record in a table to many
record of another table is called many to many
relation.
49. Database Management System
Md. Ikbal Hossain,Lecturer(ICT),Adamjee Cantonment College
P-49
Data Encryption
Encryption is the process of using an algorithm to
transform information to make it unreadable for
unauthorized users.
Process of Data Encryption
1. Caesar Code.
2. DES(Data Encryption Standard)
3.IDEA(International Data Encryption Algorithm)
50. Database Management System
Md. Ikbal Hossain,Lecturer(ICT),Adamjee Cantonment College
P-50
• Caesar Code.
Encryption Algorithm:
EN (X)=(X+N)mod 26
Where, En (X)=Cipher text or encrypted text
X=Plain Text positional value(0 to 25)
N=1,2,3………
51. Database Management System
Md. Ikbal Hossain,Lecturer(ICT),Adamjee Cantonment College
P-51
• Caesar Code.
Decryption Algorithm:
DN (X)=(X-N)mod 26
Where, DN (X)=Decrypted text
X=Plain Text positional value(0 to 25)
N=1,2,3………
52. Database Management System
Md. Ikbal Hossain,Lecturer(ICT),Adamjee Cantonment College
P-52
Encrypt the word CAESAR
Plain Text(X) Positional
Value of X
X+N
Where N=20
(X+N)mod 26 Encrypted text
EN (X)
C 2 22 22 W
A 0 20 20 U
E 4 24 24 Y
S 18 38 12 M
A 0 20 20 U
R 17 37 11 L