Abstract:
In this talk, we briefly introduce two major research problems involving databases and functional dependencies. First, we introduce an information-theoretic measure that evaluates a database design based on the worst possible redundancy carried in the instances. Then we propose new design guidelines to reduce the amount of redundancy that databases carry due to the presence of functional dependencies.
We also introduce the problem of repairing an inconsistent database that violates a set of functional dependencies by making the smallest possible value modifications. We show that finding an optimum solution is NP-hard. Then we explore the possibility of producing an approximate solution that can be used in data cleaning systems.
A statistical and schema independent approach to determine equivalent properties between linked datasets. The approach utilizes interlinking between datasets and property extensions to understand the equivalence of properties.
This working document offers a conceptual framework for understanding the processes underpinning the external dimension of EU Justice and Home Affairs (ED-JHA). Practically, it defines how the export of JHA principles and norms inform the geopolitical ambitions of the EU, i.e. the use of space for political purposes, or the control and management of people, objects and movement. The author begins by investigating how the ENP reconfigures the ED-JHA, and then goes on to discuss various conceptual stances on governance, specifically institutionalism, constructivism, and policy instruments. To conclude he traces the evolution of this external dimension, emphasising, whenever possible, its continuities and bifurcations. Overall, the aim is to ascertain the extent to which conceptual designs clarify or advance our knowledge of the contents and rationales of the ED-JHA.
Authored by: Thierry Balzacq
Published in 2008
This document discusses relational database design theory and normalization. It covers topics like first normal form, functional dependencies, attribute closure, canonical covers, decomposition, and Boyce-Codd normal form. An example university schema is provided to illustrate some concepts. The document suggests decomposing some relations in the schema to eliminate redundancy and preserve dependencies and information.
This document provides an overview of relational database design and normalization. It discusses the goals of database design as generating schemas without unnecessary redundancy and allowing easy data retrieval. Normalization aims to design schemas in a desirable normal form, such as Boyce-Codd normal form (BCNF) or third normal form (3NF). The document introduces key concepts like functional dependencies, normal forms, decomposition, and closure of functional dependencies, which are used to determine if a schema is properly normalized and how to decompose schemas if necessary.
The document discusses normalization forms up to 5NF. It explains key concepts like functional dependencies (FDs), multivalued dependencies (MVDs), and join dependencies (JDs). A example relation CTX storing courses, teachers and textbooks is used to illustrate these concepts. CTX is not in 4NF due to a non-trivial MVD, and is decomposed into binary relations CT and CX which are in BCNF and 4NF. JDs generalize MVDs and FDs, with an example relation SPJ requiring decomposition due to a JD not implied by its candidate keys.
This document provides a summary of Lecture 5 of the CS 222 Database Management System course at NIT Rourkela during the Spring 2010-2011 semester. The lecture covers database design through decomposition, including relation decomposition, lossless joins, dependency preservation, and various normal forms like 1NF, 2NF, 3NF, BCNF, and 4NF. Examples are provided to illustrate decomposition based on functional dependencies and testing for lossless joins and dependency preservation.
The document discusses database design and normalization. It introduces relational database models (RDBM) which store data in tables with columns and rows. Normalization is defined as converting relations with redundant data into simpler structures with minimum redundancy. Functional dependencies and second normal form (2NF) are explained as ways to remove redundancy during normalization. An example shows decomposing a relation that is not in 2NF by identifying determinants.
The document discusses database schema refinement through normalization. It introduces the concepts of functional dependencies and normal forms including 1NF, 2NF, 3NF and BCNF. Decomposition is presented as a technique to resolve issues like redundancy, update anomalies and insertion/deletion anomalies that arise due to violations of normal forms. Reasoning about functional dependencies and computing their closure is also covered.
A statistical and schema independent approach to determine equivalent properties between linked datasets. The approach utilizes interlinking between datasets and property extensions to understand the equivalence of properties.
This working document offers a conceptual framework for understanding the processes underpinning the external dimension of EU Justice and Home Affairs (ED-JHA). Practically, it defines how the export of JHA principles and norms inform the geopolitical ambitions of the EU, i.e. the use of space for political purposes, or the control and management of people, objects and movement. The author begins by investigating how the ENP reconfigures the ED-JHA, and then goes on to discuss various conceptual stances on governance, specifically institutionalism, constructivism, and policy instruments. To conclude he traces the evolution of this external dimension, emphasising, whenever possible, its continuities and bifurcations. Overall, the aim is to ascertain the extent to which conceptual designs clarify or advance our knowledge of the contents and rationales of the ED-JHA.
Authored by: Thierry Balzacq
Published in 2008
This document discusses relational database design theory and normalization. It covers topics like first normal form, functional dependencies, attribute closure, canonical covers, decomposition, and Boyce-Codd normal form. An example university schema is provided to illustrate some concepts. The document suggests decomposing some relations in the schema to eliminate redundancy and preserve dependencies and information.
This document provides an overview of relational database design and normalization. It discusses the goals of database design as generating schemas without unnecessary redundancy and allowing easy data retrieval. Normalization aims to design schemas in a desirable normal form, such as Boyce-Codd normal form (BCNF) or third normal form (3NF). The document introduces key concepts like functional dependencies, normal forms, decomposition, and closure of functional dependencies, which are used to determine if a schema is properly normalized and how to decompose schemas if necessary.
The document discusses normalization forms up to 5NF. It explains key concepts like functional dependencies (FDs), multivalued dependencies (MVDs), and join dependencies (JDs). A example relation CTX storing courses, teachers and textbooks is used to illustrate these concepts. CTX is not in 4NF due to a non-trivial MVD, and is decomposed into binary relations CT and CX which are in BCNF and 4NF. JDs generalize MVDs and FDs, with an example relation SPJ requiring decomposition due to a JD not implied by its candidate keys.
This document provides a summary of Lecture 5 of the CS 222 Database Management System course at NIT Rourkela during the Spring 2010-2011 semester. The lecture covers database design through decomposition, including relation decomposition, lossless joins, dependency preservation, and various normal forms like 1NF, 2NF, 3NF, BCNF, and 4NF. Examples are provided to illustrate decomposition based on functional dependencies and testing for lossless joins and dependency preservation.
The document discusses database design and normalization. It introduces relational database models (RDBM) which store data in tables with columns and rows. Normalization is defined as converting relations with redundant data into simpler structures with minimum redundancy. Functional dependencies and second normal form (2NF) are explained as ways to remove redundancy during normalization. An example shows decomposing a relation that is not in 2NF by identifying determinants.
The document discusses database schema refinement through normalization. It introduces the concepts of functional dependencies and normal forms including 1NF, 2NF, 3NF and BCNF. Decomposition is presented as a technique to resolve issues like redundancy, update anomalies and insertion/deletion anomalies that arise due to violations of normal forms. Reasoning about functional dependencies and computing their closure is also covered.
The document discusses database normalization through three forms:
1) First normal form (1NF) involves eliminating repeating groups and defining primary keys so that each attribute depends on the full primary key.
2) Second normal form (2NF) builds on 1NF and removes partial dependencies by splitting tables where attributes depend on only part of a composite primary key.
3) Third normal form (3NF) builds on 2NF and removes transitive dependencies by splitting tables where a non-key attribute depends on another non-key attribute rather than the primary key. The goal is to isolate each functional dependency and minimize data anomalies.
The document provides an overview of functional dependencies and database normalization. It discusses four informal design guidelines for relational databases: 1) design relations so their meaning is clear, 2) avoid anomalies, 3) avoid null values, and 4) avoid spurious tuples. It then covers functional dependencies, inference rules, equivalence, and normal forms including 1NF, 2NF, 3NF and BCNF. The goals of normalization are also summarized as reducing redundancy, anomalies, and producing high quality schemas. Examples are provided to illustrate each concept.
The document discusses normalization of database relations through various normal forms. It defines first normal form (1NF) as removing repeating groups from relations by either adding empty columns or creating a separate relation. Second normal form (2NF) requires that relations in 1NF have no partial dependencies, where non-key attributes must fully depend on the primary key. The document provides examples of relations in 1NF and 2NF and discusses functional dependencies.
Functional dependencies play a key role in database design and normalization. A functional dependency (FD) is a constraint that one attribute determines another. FDs have various definitions but generally mean that given the value of one attribute (left side), the value of another attribute (right side) is determined. Armstrong's axioms are used to derive implied FDs from a set of FDs. The closure of an attribute set or set of FDs finds all attributes/FDs logically implied. Normalization aims to eliminate anomalies and is assessed using normal forms like 1NF, 2NF, 3NF, BCNF which impose additional constraints on table designs.
Functional dependency defines a relationship between attributes in a table where a set of attributes determine another attribute. There are different types of functional dependencies including trivial, non-trivial, multivalued, and transitive. An example given is a student table with attributes Stu_Id, Stu_Name, Stu_Age which has the functional dependency of Stu_Id->Stu_Name since the student ID uniquely identifies the student name.
Functional dependencies and normalization for relational databasesJafar Nesargi
This document discusses guidelines for designing relational databases. It covers four informal measures of quality: semantics of attributes, reducing redundancy, reducing null values, and avoiding spurious tuples. The guidelines are: 1) design relations so their meaning is clear, 2) avoid anomalies like insertion, deletion and modification anomalies, 3) minimize null values in attributes, and 4) design relations to join without generating spurious tuples. The document uses examples to illustrate these concepts and their importance for database design.
Content of slide
Tree
Binary tree Implementation
Binary Search Tree
BST Operations
Traversal
Insertion
Deletion
Types of BST
Complexity in BST
Applications of BST
Michael Joseph is giving a presentation on database normalization. He begins by explaining the importance of properly structuring data across database tables and the problems that can arise from poor database design, such as redundancy, inaccuracy, and consistency issues. He then describes database normalization as a process that organizes data to minimize redundancy by decomposing relations and isolating data in separate, well-defined tables connected through relationships. Different levels of normalization are discussed, with third normal form being sufficient for most applications. Examples are provided to illustrate how normalization progresses from first to third normal form. Potential issues with highly normalized databases are also outlined.
The document discusses various tree data structures and algorithms related to binary trees. It begins with an introduction to different types of binary trees such as strict binary trees, complete binary trees, and extended binary trees. It then covers tree traversal algorithms including preorder, inorder and postorder traversal. The document also discusses representations of binary trees using arrays and linked lists. Finally, it explains algorithms for operations on binary search trees such as searching, insertion, deletion and rebalancing through rotations in AVL trees.
Database Normalization
The term Normalization is a process by which we can efficiently organize the data in a database. It associates relationship between individual tables according to policy designed both to care for the data and to create the database more flexible by eliminating redundancy and inconsistent dependency.
In other words, Database normalization is a process by which a presented database is tailored to bring its component tables into compliance with a sequence of progressive standard forms. It is an organized way of ensuring that a database construction is appropriate for general purpose querying and also includes the functions of insertion, deletion and updating.
Edgar Frank Codd was the person who introduced the process of database normalization firstly in his paper called A Relational Model of Data for Large Shared Data Banks. The two main objective of database normalization is eliminating redundant data and ensuring data dependencies make sense and make sure that every non-key column in every table is directly reliant on the key and the whole key.
Redundant data or unnecessary data will take more and more space in the database and later, creates the maintenance problem in the database. If data that exists in more than one place must be changed because it wastes disk space and the data must be changed in exactly the same way in all locations of the table.
The document discusses relational database design and normalization. It covers first normal form, functional dependencies, and decomposition. The goal of normalization is to avoid data redundancy and anomalies. First normal form requires attributes to be atomic. Functional dependencies specify relationships between attributes that must be preserved. Decomposition breaks relations into smaller relations while maintaining lossless join properties. Higher normal forms like Boyce-Codd normal form and third normal form further reduce redundancy.
Normalization is a technique for designing relational database tables to minimize duplication of data and ensure data integrity. It involves organizing data into tables and establishing relationships between tables based on their attributes. There are several normal forms like 1NF, 2NF and 3NF that provide rules for table design to reduce anomalies and inconsistencies. Functional dependencies define relationships between attributes in a table, and normalization aims to remove non-key attributes that are functionally dependent on other attributes.
The document discusses normalization in database design. Normalization is the process of organizing data to avoid redundancy and dependency. It involves splitting tables and restructuring relationships between tables. The document outlines various normal forms including 1NF, 2NF, 3NF, BCNF, 4NF and 5NF and provides examples to illustrate how to normalize tables to conform to each form.
SIGMOD 2013 - Patricia's talk on "Value invention for Data Exchange"Boris Glavic
The document discusses value invention in data exchange and schema mappings. It introduces the data exchange problem involving mapping source and target schemas using a specification. Value invention involves creating values to represent incomplete information when materializing the target schema. The goal is to understand when schema mappings specified by second-order tuple-generating dependencies (SO tgds) can be rewritten as nested global-as-view mappings, which have more desirable computational properties. The paper presents an algorithm called Linearize that rewrites SO tgds as nested GLAV mappings if they are linear and consistent. It also discusses exploiting source constraints like functional dependencies to find an equivalent linear mapping.
Tutorial presented at 2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012), January 28-30, 2012. http://sites.google.com/site/web2011ihi/participants/tutorials
This tutorial weaves together three themes and the associated topics:
[1] The role of biomedical ontologies
[2] Key Semantic Web technologies with focus on Semantic provenance and integration
[3] In-practice tools and real world use cases built to serve the needs of sleep medicine researchers, cardiologists involved in clinical practice, and work on vaccine development for human pathogens.
Presentation on Graph Clustering (vldb 09)Waqas Nawaz
This document proposes a graph clustering method that considers both structural and attribute similarities among nodes. It augments the original graph by adding attribute nodes and edges. A unified neighborhood random walk distance is used to measure node closeness on the augmented graph. Edge weights are automatically adjusted during clustering to optimize the contributions of different attributes. Experimental results on real datasets demonstrate that the proposed method achieves better balance between structural cohesiveness and attribute homogeneity compared to other methods.
Property graph vs. RDF Triplestore comparison in 2020Ontotext
This presentation goes all the way from intro "what graph databases are" to table comparing the RDF vs. PG plus two different diagrams presenting the market circa 2020
This document discusses challenges and opportunities for libraries in managing metadata in the new era of linked data. It addresses issues like shifting from thinking about records to statements, extending vocabularies, mapping legacy data for redistribution, and evaluating and improving metadata through iterative processes. The focus is on distributing data openly and empowering users and other organizations to make use of library metadata through flexible models rather than centralized systems.
SSONDE is a framework for calculating semantic similarity between ontology instances represented as linked data. It provides an asymmetric similarity score that emphasizes containment relationships between instances. SSONDE operates at the application layer and assumes integration steps like ontology alignment have already occurred. It has been applied to compare researchers based on publications and interests, and habitats based on hosted species. The framework supports configurable similarity contexts and caching to optimize performance on large linked datasets.
Sparql semantic information retrieval byIJNSA Journal
Semantic web document representation is formulated using RDF/OWL. RDF representation is in the form
of triples & OWL in form of ontologies. The above representation leads to a data set which needs to be
queried using software agents, machines. W3C has recommended SPARQL to be the de facto query
language for RDF. This paper proposes to suggest a model to enable SPARQL to make search efficient,
easier and produce meaningful search distinguished on the basis of preposition. An RDF data source
primarily consist of data that is represented in the form of a Triple pattern which has an appropriate RDF
syntax and further results into an RDF Graph .The RDF repository stores the Data on Subject, Predicate,
Object Model. The Predicate may also be thought of as a property linking the Subject and Object. This
paper shall evaluate the information retrieval by incorporating preposition as property.
The document discusses database normalization through three forms:
1) First normal form (1NF) involves eliminating repeating groups and defining primary keys so that each attribute depends on the full primary key.
2) Second normal form (2NF) builds on 1NF and removes partial dependencies by splitting tables where attributes depend on only part of a composite primary key.
3) Third normal form (3NF) builds on 2NF and removes transitive dependencies by splitting tables where a non-key attribute depends on another non-key attribute rather than the primary key. The goal is to isolate each functional dependency and minimize data anomalies.
The document provides an overview of functional dependencies and database normalization. It discusses four informal design guidelines for relational databases: 1) design relations so their meaning is clear, 2) avoid anomalies, 3) avoid null values, and 4) avoid spurious tuples. It then covers functional dependencies, inference rules, equivalence, and normal forms including 1NF, 2NF, 3NF and BCNF. The goals of normalization are also summarized as reducing redundancy, anomalies, and producing high quality schemas. Examples are provided to illustrate each concept.
The document discusses normalization of database relations through various normal forms. It defines first normal form (1NF) as removing repeating groups from relations by either adding empty columns or creating a separate relation. Second normal form (2NF) requires that relations in 1NF have no partial dependencies, where non-key attributes must fully depend on the primary key. The document provides examples of relations in 1NF and 2NF and discusses functional dependencies.
Functional dependencies play a key role in database design and normalization. A functional dependency (FD) is a constraint that one attribute determines another. FDs have various definitions but generally mean that given the value of one attribute (left side), the value of another attribute (right side) is determined. Armstrong's axioms are used to derive implied FDs from a set of FDs. The closure of an attribute set or set of FDs finds all attributes/FDs logically implied. Normalization aims to eliminate anomalies and is assessed using normal forms like 1NF, 2NF, 3NF, BCNF which impose additional constraints on table designs.
Functional dependency defines a relationship between attributes in a table where a set of attributes determine another attribute. There are different types of functional dependencies including trivial, non-trivial, multivalued, and transitive. An example given is a student table with attributes Stu_Id, Stu_Name, Stu_Age which has the functional dependency of Stu_Id->Stu_Name since the student ID uniquely identifies the student name.
Functional dependencies and normalization for relational databasesJafar Nesargi
This document discusses guidelines for designing relational databases. It covers four informal measures of quality: semantics of attributes, reducing redundancy, reducing null values, and avoiding spurious tuples. The guidelines are: 1) design relations so their meaning is clear, 2) avoid anomalies like insertion, deletion and modification anomalies, 3) minimize null values in attributes, and 4) design relations to join without generating spurious tuples. The document uses examples to illustrate these concepts and their importance for database design.
Content of slide
Tree
Binary tree Implementation
Binary Search Tree
BST Operations
Traversal
Insertion
Deletion
Types of BST
Complexity in BST
Applications of BST
Michael Joseph is giving a presentation on database normalization. He begins by explaining the importance of properly structuring data across database tables and the problems that can arise from poor database design, such as redundancy, inaccuracy, and consistency issues. He then describes database normalization as a process that organizes data to minimize redundancy by decomposing relations and isolating data in separate, well-defined tables connected through relationships. Different levels of normalization are discussed, with third normal form being sufficient for most applications. Examples are provided to illustrate how normalization progresses from first to third normal form. Potential issues with highly normalized databases are also outlined.
The document discusses various tree data structures and algorithms related to binary trees. It begins with an introduction to different types of binary trees such as strict binary trees, complete binary trees, and extended binary trees. It then covers tree traversal algorithms including preorder, inorder and postorder traversal. The document also discusses representations of binary trees using arrays and linked lists. Finally, it explains algorithms for operations on binary search trees such as searching, insertion, deletion and rebalancing through rotations in AVL trees.
Database Normalization
The term Normalization is a process by which we can efficiently organize the data in a database. It associates relationship between individual tables according to policy designed both to care for the data and to create the database more flexible by eliminating redundancy and inconsistent dependency.
In other words, Database normalization is a process by which a presented database is tailored to bring its component tables into compliance with a sequence of progressive standard forms. It is an organized way of ensuring that a database construction is appropriate for general purpose querying and also includes the functions of insertion, deletion and updating.
Edgar Frank Codd was the person who introduced the process of database normalization firstly in his paper called A Relational Model of Data for Large Shared Data Banks. The two main objective of database normalization is eliminating redundant data and ensuring data dependencies make sense and make sure that every non-key column in every table is directly reliant on the key and the whole key.
Redundant data or unnecessary data will take more and more space in the database and later, creates the maintenance problem in the database. If data that exists in more than one place must be changed because it wastes disk space and the data must be changed in exactly the same way in all locations of the table.
The document discusses relational database design and normalization. It covers first normal form, functional dependencies, and decomposition. The goal of normalization is to avoid data redundancy and anomalies. First normal form requires attributes to be atomic. Functional dependencies specify relationships between attributes that must be preserved. Decomposition breaks relations into smaller relations while maintaining lossless join properties. Higher normal forms like Boyce-Codd normal form and third normal form further reduce redundancy.
Normalization is a technique for designing relational database tables to minimize duplication of data and ensure data integrity. It involves organizing data into tables and establishing relationships between tables based on their attributes. There are several normal forms like 1NF, 2NF and 3NF that provide rules for table design to reduce anomalies and inconsistencies. Functional dependencies define relationships between attributes in a table, and normalization aims to remove non-key attributes that are functionally dependent on other attributes.
The document discusses normalization in database design. Normalization is the process of organizing data to avoid redundancy and dependency. It involves splitting tables and restructuring relationships between tables. The document outlines various normal forms including 1NF, 2NF, 3NF, BCNF, 4NF and 5NF and provides examples to illustrate how to normalize tables to conform to each form.
SIGMOD 2013 - Patricia's talk on "Value invention for Data Exchange"Boris Glavic
The document discusses value invention in data exchange and schema mappings. It introduces the data exchange problem involving mapping source and target schemas using a specification. Value invention involves creating values to represent incomplete information when materializing the target schema. The goal is to understand when schema mappings specified by second-order tuple-generating dependencies (SO tgds) can be rewritten as nested global-as-view mappings, which have more desirable computational properties. The paper presents an algorithm called Linearize that rewrites SO tgds as nested GLAV mappings if they are linear and consistent. It also discusses exploiting source constraints like functional dependencies to find an equivalent linear mapping.
Tutorial presented at 2012 ACM SIGHIT International Health Informatics Symposium (IHI 2012), January 28-30, 2012. http://sites.google.com/site/web2011ihi/participants/tutorials
This tutorial weaves together three themes and the associated topics:
[1] The role of biomedical ontologies
[2] Key Semantic Web technologies with focus on Semantic provenance and integration
[3] In-practice tools and real world use cases built to serve the needs of sleep medicine researchers, cardiologists involved in clinical practice, and work on vaccine development for human pathogens.
Presentation on Graph Clustering (vldb 09)Waqas Nawaz
This document proposes a graph clustering method that considers both structural and attribute similarities among nodes. It augments the original graph by adding attribute nodes and edges. A unified neighborhood random walk distance is used to measure node closeness on the augmented graph. Edge weights are automatically adjusted during clustering to optimize the contributions of different attributes. Experimental results on real datasets demonstrate that the proposed method achieves better balance between structural cohesiveness and attribute homogeneity compared to other methods.
Property graph vs. RDF Triplestore comparison in 2020Ontotext
This presentation goes all the way from intro "what graph databases are" to table comparing the RDF vs. PG plus two different diagrams presenting the market circa 2020
This document discusses challenges and opportunities for libraries in managing metadata in the new era of linked data. It addresses issues like shifting from thinking about records to statements, extending vocabularies, mapping legacy data for redistribution, and evaluating and improving metadata through iterative processes. The focus is on distributing data openly and empowering users and other organizations to make use of library metadata through flexible models rather than centralized systems.
SSONDE is a framework for calculating semantic similarity between ontology instances represented as linked data. It provides an asymmetric similarity score that emphasizes containment relationships between instances. SSONDE operates at the application layer and assumes integration steps like ontology alignment have already occurred. It has been applied to compare researchers based on publications and interests, and habitats based on hosted species. The framework supports configurable similarity contexts and caching to optimize performance on large linked datasets.
Sparql semantic information retrieval byIJNSA Journal
Semantic web document representation is formulated using RDF/OWL. RDF representation is in the form
of triples & OWL in form of ontologies. The above representation leads to a data set which needs to be
queried using software agents, machines. W3C has recommended SPARQL to be the de facto query
language for RDF. This paper proposes to suggest a model to enable SPARQL to make search efficient,
easier and produce meaningful search distinguished on the basis of preposition. An RDF data source
primarily consist of data that is represented in the form of a Triple pattern which has an appropriate RDF
syntax and further results into an RDF Graph .The RDF repository stores the Data on Subject, Predicate,
Object Model. The Predicate may also be thought of as a property linking the Subject and Object. This
paper shall evaluate the information retrieval by incorporating preposition as property.
Top-N Recommendations from Implicit Feedback leveraging Linked Open DataVito Ostuni
The document describes a new approach called SPrank for top-N recommendations from implicit feedback using linked open data. SPrank analyzes relationships between user preferences and items through path-based features extracted from a knowledge graph. A learning to rank method is used to learn the ranking function from these features. Experimental results on movie and music datasets mapped to DBpedia show SPrank outperforms other recommendation techniques, particularly with smaller user profiles.
Ways to Extract Variable Insights when Data is ScarseZia Babar
The document discusses ways to extract insights from scarce data using machine learning and other techniques. It describes a dataset of Freedom of Information requests with few records. Machine learning performed poorly due to the small dataset size. However, descriptive statistics, exploratory data analysis, and natural language processing tools like n-grams and topic modeling can still provide valuable information. These alternative techniques were demonstrated on the request data through word clouds, frequency analysis, and topic modeling.
The document summarizes research on performing spatio-textual similarity joins. It discusses:
1) Developing a filter-and-refine framework to efficiently find similar object pairs from two datasets using signatures.
2) Generating spatial and textual signatures for objects and building inverted indexes on the signatures to find candidate pairs.
3) Refining the candidate pairs to obtain the final result pairs that satisfy spatial and textual similarity thresholds.
This document describes a middleware called SWING that integrates the data mining system AL-QUIN and the ontological engineering tool Protege-2000. This integration enables AL-QUIN to perform semantic web mining tasks by making it compliant with Semantic Web standards and interoperable with Protege-2000. The middleware suggests a methodology for building semantic web mining systems by upgrading existing data mining systems to work with ontological engineering tools.
Mapping of extensible markup language-to-ontology representation for effectiv...IAESIJAI
Extensible markup language (XML) is well-known as the standard for data exchange over the internet. It is flexible and has high expressibility to express the relationship between the data stored. Yet, the structural complexity and the semantic relationships are not well expressed. On the other hand, ontology models the structural, semantic and domain knowledge effectively. By combining ontology with visualization effect, one will be able to have a closer view based on respective user requirements. In this paper, we propose several mapping rules for the transformation of XML into ontology representation. Subsequently, we show how the ontology is constructed based on the proposed rules using the sample domain ontology in University of Wisconsin-Milwaukee (UWM) and mondial datasets. We
also look at the schemas, query workload, and evaluation, to derive the extended knowledge from the existing ontology. The correctness of the ontology representation has been proven effective through supporting various types of complex queries in simple protocol and resource description framework query language (SPARQL) language.
Effective Semantics for Engineering NLP SystemsAndre Freitas
Provide a synthesis of the emerging representation trends behind NLP systems.
Shift in perspective:
Effective engineering (task driven, scalable) instead of sound formalism.
Best-effort representation.
Knowledge Graphs (Frege revisited)
Information Extraction & Text Classification
Distributional Semantic Models
Knowledge Graphs & Distributional Semantics
(Distributional-Relational Models)
Applications of DRMs
KG Completion
Semantic Parsing
Natural Language Inference
This document discusses relational databases, RDF graphs, and constraints. It covers:
- Relational databases and their use of constraints like primary keys
- RDF graphs and their lack of explicit schema/constraints
- Mappings from relational databases to RDF graphs using direct mapping and R2RML
- Approaches to rewrite database constraints to SHACL constraints to validate the mapped RDF graph
- Opportunities to optimize SPARQL queries using inferred constraints from the SHACL shapes
Wi2015 - Clustering of Linked Open Data - the LODeX toolLaura Po
Presentation of the tool LODeX (http://www.dbgroup.unimore.it/lodex2/testCluster) at the 2015 IEEE/WIC/ACM International Conference on Web Intelligence, Singapore, December 6-8, 2015
Image-Based Literal Node Matching for Linked Data IntegrationIJwest
This paper proposes a method of identifying and aggregating literal nodes that have the same meaning in Linked Open Data (LOD) in order to facilitate cross-domain search. LOD has a graph structure in which most nodes are represented by Uniform Resource Identifiers (URIs), and thus LOD sets are connected and searched through different domains.However, 5% of the values are literal values (strings without URI) even in a de facto hub of LOD, DBpedia. In SPARQL Protocol and RDF Query Language (SPARQL) queries, we need to rely on regular expression to match and trace the literal nodes. Therefore, we propose a novel method, in which part of the LOD graph structure is regarded as a block image, and then the matching is calculated by image features of LOD. In experiments, we created about 30,000 literal pairs from a Japanese music category of DBpedia Japanese and Freebase, and confirmed that the proposed method determines literal identity with F-measure of 76.1-85.0%.
SPARQL: SEMANTIC INFORMATION RETRIEVAL BY EMBEDDING PREPOSITIONSIJNSA Journal
This document discusses incorporating prepositions into SPARQL queries to enable more semantic searching of RDF datasets. It proposes treating prepositions as properties in RDF triples. Currently, SPARQL cannot distinguish search results based on prepositions. The paper describes representing RDF data as subject-predicate-object triples and graphs. It also explains the basic structure of SPARQL queries and architecture. By specifying prepositions as properties in an RDF schema, SPARQL could return search results based on the preposition between keywords. This would require RDF datasets to define schemas accounting for prepositions to fully enable preposition-based semantic searches with SPARQL.
Collaborative Similarity Measure for Intra-Graph ClusteringWaqas Nawaz
The document summarizes a presentation on a proposed collaborative similarity measure (CSM) for intra-graph clustering. CSM calculates similarity between vertices based on both their structural proximity and attribute similarity. It was tested on real and synthetic datasets and was shown to be scalable to medium graphs while maintaining high quality clusters, as measured by density, entropy, and F-measure, compared to other methods. The presentation covered the motivation, related work, CSM method, experiments evaluating time complexity and quality, and conclusions.
Nicoletta Fornara and Fabio Marfia | Modeling and Enforcing Access Control Ob...semanticsconference
This document discusses modeling and enforcing access control obligations for SPARQL-DL queries. It proposes an approach using formal specifications of obligations to define fine-grained access control for inferred data in OWL 2 DL ontologies. An obligation enforcement module sits as a middle layer, rewriting queries before execution and enforcing obligations on results by modifying returned data based on obligation definitions. The approach allows complex queries while protecting inferred data through reasoning about access control conditions.
Similar to Somaz Kolahi : Functional Dependencies: Redundancy Analysis and Correcting Violations (20)
Speaker: Dr. Mohammad Noshad
Postdoctoral Fellow
Department of Electrical Engineering
Harvard University, Cambridge, USA
Title: High-Speed Wireless Connectivity through Lights
Time: Saturday, February 4, 2017, 12:30 – 14:00
Location: School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
Ali khalili: Towards an Open Linked Data-based Infrastructure for Studying Sc...knowdiff
This document proposes Linked Data-driven Web Components (LD-R) to build flexible and reusable user interfaces for Semantic Web applications. LD-R uses semantic markup, configurations and scopes to create reusable RDF and user-defined components. It implements a reactive architecture with Linked Data, microservices and isomorphic components. Example uses of LD-R include facets browsers and editing interfaces for datasets. The document concludes that LD-R bridges Semantic Web technologies and Web Components to provide richer discovery, integration and adaptation of components while improving standardization and reusability of Semantic Web application user interfaces.
Scheduling for cloud systems with multi level data localityknowdiff
Speaker: Ali Yekkehkhany
(1)Time: Monday, Jan 4, 2016, 13:00- 15:00
(1)Location: School of Electrical Engineering, Iran University of Science and Technology
(2)Time: Tuesday, Jan 12, 2016, 12:30- 14:00
(2)Location: School of Electrical and Computer Engineering, University of Tehran
Amin Milani Fard: Directed Model Inference for Testing and Analysis of Web Ap...knowdiff
The document discusses automated testing techniques for web applications. It proposes feedback-directed exploration to generate test models more effectively than exhaustive crawling. It also leverages existing manual tests to generate new automated tests by reusing inputs, assertions and exploring alternative paths. A technique called ConFix is presented to automatically generate DOM-based fixtures for unit tests by collecting constraints from code instrumentation. Finally, the document discusses detecting prevalent JavaScript code smells like lazy objects to support automated refactoring.
Knowledge based economy and power of crowd sourcing knowdiff
Patexia is a platform that connects IP-intensive businesses to subject matter experts to provide patent research and analysis through crowdsourcing. The document discusses how the global economy has transitioned to a knowledge-based economy where intellectual property plays a key role. It then provides an overview of Patexia, including its history, mission to bring more transparency and efficiency to IP using technology and collaboration, and the services it offers clients in areas like patent research, IP generation, monetization, protection, and management by building a bridge between solvers in the science/tech community and problems faced by IP organizations.
Amin tayyebi: Big Data and Land Use Change Scienceknowdiff
Ph.D.
University of California-Riverside, Center for Conservation Biology
1)Time: Tuesday, August 25, 2015, 15:30- 16:30
(1)Location: Amirkabir University of Technology, Department of Civil and Environmental Engineering
(2)Time: Wednesday, August 26, 2015, 14:00- 16:00
(2)Location: Department of Surveying Engineering, University of Tehran, N. Kargar St.
Mehdi Rezagholizadeh: Image Sensor Modeling: Color Measurement at Low Light L...knowdiff
Ph.D. Candidate, Electrical and Computer Engineering,
Center for Intelligent Machines (CIM)
McGill University
(1) Time: Wednesday, Dec. 17th, 12:30-14:30 pm
(1) Location: faculty’s conference room, Isfahan University of Technology
(2) Time: Tuesday, Dec. 9th, 12:30-14:00pm
(2) Location: Room 212, School of Electrical and Computer Engineering of University of Tehran
Abstract:
Investigating low light imaging is of high importance in the field of color science from different perspectives. One of the most important challenges arises at low light levels is the issue of noise, or more generally speaking, low signal to noise ratio. In the present work, effects of different image sensor noises such as: photon noise, dark current noise, read noise, and quantization error are investigated on low light color measurements. In this regard, a typical image sensor is modeled and employed for this study. A detailed model of noise is considered in the process of implementing the image sensor model to guarantee the precision of the results. Several experiments have been performed over the implemented framework and the results show that: first, photon noise, read noise, and quantization error lead to uncertain measurements distributed around the noise free measurements and these noisy samples form an elliptical shape in the chromaticity diagram; second, even for an ideal image sensor, in very dark situations, stable measuring of color is impossible due to the physical limitation imposed by the fluctuations in photon emission rate; third, dark current noise reveals dynamic effects on color measurements by shifting their chromaticities towards the chromaticity of the camera black point; fourth, dark current dominates the other sensor noise types in the image sensor in terms of affecting measurements. Moreover, an SNR sensitivity analysis against the noise parameters is presented over different light intensities.
Sara Afshar: Scheduling and Resource Sharing in Multiprocessor Real-Time Systemsknowdiff
PhD Candidate,
Department of Computer science
Mälardalen University
Time: Tuesday, Dec. 30, 2014, 11:30 a.m.
Location: Computer Engineering Department, Urmia University
Abstract:
The processor is the brain of a computer system. Usually, one or more programs run on a processor where each program is typically responsible for performing a particular task or function of the system. The performance of all the tasks together results in the system functionality. In many computer systems, it is not only enough that all tasks deliver correct output, but it is also crucial that these activities are delivered in a proper time. This type of systems that have timing requirements are known as real-time systems. A scheduler is responsible for scheduling all tasks on the processor, i.e., it dictates which task to run and when to run to ensure that all tasks are carried out on time. Typically, such tasks/programs need to use the computer system’s hardware and software resources to perform their calculation. Examples of such type of resources that are shared among programs are I/O devices, buffers and memories. Technology that is used for the management of shared resources is known as resource sharing synchronization protocol.
In recent years, a shift from single-processor platforms to multiprocessor platforms has become inevitable due to availability of processor chips and requirements on increased performance. Scheduling and resource sharing protocols have been well studied for uniprocessor systems. However, in the context of multiprocessors, still such techniques are not fully mature. The shift towards multi-core technology has revealed the demand for real-time scheduling algorithms along with synchronization protocols to support real-time applications on multiprocessors, both with and without dependencies.
In this talk, we first have an introduction to real-time embedded systems. Next, we look at scheduling and resource sharing policies in uniprocessor platforms. Further, we discuss the extension of scheduling and resource sharing policies for multiprocessor platforms and present the recent challenges arisen in this context.
Biography:
Sara Afshar is a PhD student at Mälardalen University. She has received her B.Sc. degree in Electrical Engineering from Tabriz University, Iran in 2002. She worked at different engineering companies until 2009. In the year 2010 she started her M.Sc. in Embedded Systems at Mälardalen University. She obtained her Master degree in 2012 and at the same year she started her PhD studies in Mälardalen University. Currently she is working on the topic of resource sharing in multiprocessor systems. She is part of the Complex Real-Time Embedded Systems group at Mälardalen University.
Seyed Mehdi mohaghegh: Modelling material use within the low carbon energy pa...knowdiff
PhD student,
UCL Energy Institute
University College London (UCL)
Time: Monday, January 5, 2015 at 14:00
Location: Energy Engineering Dept., Ghasemi Ave., (North wing of Sharif University of Technology). - Ground Floor - Seminar Room 1
Abstract:
The topic of “sustainability” need to be analyzed by considering the impact of such diverse sectors as energy, material, natural resources and climate systems. The important point is that due to the “hyperconnectivity” among these sectors, ignoring their interactions, dependencies, and links in transition pathways can produce catastrophic results. For this reason, some recent studies have suggested the “nexus” approach for analyzing and modelling low-carbon future scenarios. In general, in a large-scale “nexus” approach, the system deals with complexities and feedback mechanisms resulting from the interactions of diverse sectors such as climate, energy, materials, land and water. However, for this project, the primary focus is on the interaction of material and energy as an inter-sectoral segment of the nexus approach.
In this project, the goals are to (a) model the use of materials within the transition pathways generated for a low-carbon future and (b) compare the required material flow in these low-carbon pathways with the material flow in the based projections.
Some of the applications and advantages of this research include:
• Providing science-based support for policy makers regarding the required materials for low-carbon energy systems.
• Considering realistic uncertainties associated with the material flow inside energy systems and applying appropriate probabilistic methods.
• Advancing TIAM-UCL by adding the material flow module. TIAM-UCL encompasses 16 global regions and this additional module could provide a more complete analysis regarding the distribution of required material resources within energy systems, which would generate favorable options for trade and also reduce the cost of welfare.
Narjess Afzaly: Model Your Problem with Graphs and Generate your objectsknowdiff
Generating non-isomorphic (non-equivalent) graphs has many applications in industry and in different branches of science where the problem can be modeled by graphs. We discuss the importance and the difficulty of avoiding equivalent copies when generating graphs representing the objects of your interest, say protein three-dimensional structure. We then look at the techniques of generation avoiding equivalent copies.
Computational methods applications in air pollution modeling (Dr. Yadghar)knowdiff
Computational modeling of pollutant transport, dispersion and deposition is described. Particular attention is given to transport and deposition of contaminant particles in atmospheric flows around buildings, in street canyons and near bridges. The Eulerian-Eulerian and Eulerian-Lagrangian models are outlined. Particular attention was given to the use of advanced anisotropic turbulence models and a Lagrangian particle trajectory analysis. The procedure for simulating the instantaneous turbulence fluctuating velocity vector with the aid of random field model sis described. Examples of dispersion and deposition of pollutants near buildings, in street canyons and near bridges are discussed. It is shown that the computer simulation can predict the features of the experimentally observed pollutant concentration data.
Visiting Lecturer Program (140)
Speaker: Azad Shademan
Ph.D. candidate
Department of Computing Sciences
University of Alberta, Canada
Title: Uncalibrated Image-Based Robotic Visual Servoing
Local Host: Ms. Nasim Pouraryan
Time: Wednesday, November 5, 2008, 12:30-2:00 pm
Location: Faculty of Electrical and Computer Engineering, University of Tehran, Tehran
Abstract:
Design of versatile vision-based robotic systems demands a solution with little or no dependence on system parameters. The problem of real-time vision-based control of robots has been long studied as robotic visual servoing. Most provably stable solutions to this problem require calibrated kinematic and camera models, because in a precisely calibrated system one can model the visual-motor function analytically. The uncalibrated approach has received limited attention mainly because the stability analysis is not as straightforward as that of calibrated image-based architecture. In an uncalibrated system the visual-motor function is not known, but partial derivative information (Jacobian) can be learned by tracking visual measurements during motion. In this talk, we study the uncalibrated image-based visual servoing and present different Jacobian learning methods.
knowdiff.net
Design of versatile vision-based robotic systems demands a solution with little or no dependence on system parameters. The problem of real-time vision-based control of robots has been long studied as robotic visual servoing. Most provably stable solutions to this problem require calibrated kinematic and camera models, because in a precisely calibrated system one can model the visual-motor function analytically. The uncalibrated approach has received limited attention mainly because the stability analysis is not as straightforward as that of calibrated image-based architecture. In an uncalibrated system the visual-motor function is not known, but partial derivative information (Jacobian) can be learned by tracking visual measurements during motion. In this talk, we study the uncalibrated image-based visual servoing and present different Jacobian learning methods.
Speaker: Mehran Shaghaghi
Ph.D. Candidate
Department of Physics and Astronomy, University of British Columbia, Canada
Title: Quantum Mechanics Dilemmas
Organized by the Knowledge Diffusion Network
Time: Tuesday, December 11th , 2007.
Location: Department of Physics, Sharif University of Technology, Tehran
This document provides an overview of coding theory and recent advances in low-density parity-check (LDPC) codes. It discusses Shannon's channel coding theorem and how modern error-correcting codes achieve rates close to channel capacity. LDPC codes are described as having sparse parity-check matrices and being decoded iteratively using message passing. The performance of LDPC codes can be analyzed using density evolution and threshold calculations. Linear programming decoding is introduced as an alternative decoding approach that has connections to message passing decoding.
This document summarizes research on developing an efficient higher-order accurate unstructured finite volume algorithm for inviscid compressible fluid flows. The algorithm uses an ILU preconditioned GMRES method to solve the Euler equations on unstructured meshes. Higher-order solutions of up to fourth-order accuracy were obtained. Results show the third-order solution was 1.3-1.5 times more expensive than second-order, while fourth-order was 3.5-5 times more expensive, demonstrating the efficiency of the higher-order approach. Test cases included supersonic and transonic flows, with results agreeing well with structured solvers.
Knowledge Diffusion Network
Visiting Lecturer Program (114)
Speaker: Alborz Geramifard
Ph.D. Candidate
Department of Computing Science, Edmonton, University of Alberta, Canada
Title: Incremental Least- Squares Temporal Difference Learning
Time: Tuesday, Sep 11, 2007, 12:00-1:00 pm
Location: Department of Computer Engineering, Sharif University of Technology, Tehran
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...Alex Pruden
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Trusted Execution Environment for Decentralized Process MiningLucaBarbaro3
Presentation of the paper "Trusted Execution Environment for Decentralized Process Mining" given during the CAiSE 2024 Conference in Cyprus on June 7, 2024.
Digital Banking in the Cloud: How Citizens Bank Unlocked Their MainframePrecisely
Inconsistent user experience and siloed data, high costs, and changing customer expectations – Citizens Bank was experiencing these challenges while it was attempting to deliver a superior digital banking experience for its clients. Its core banking applications run on the mainframe and Citizens was using legacy utilities to get the critical mainframe data to feed customer-facing channels, like call centers, web, and mobile. Ultimately, this led to higher operating costs (MIPS), delayed response times, and longer time to market.
Ever-changing customer expectations demand more modern digital experiences, and the bank needed to find a solution that could provide real-time data to its customer channels with low latency and operating costs. Join this session to learn how Citizens is leveraging Precisely to replicate mainframe data to its customer channels and deliver on their “modern digital bank” experiences.
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
A Comprehensive Guide to DeFi Development Services in 2024Intelisync
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Somaz Kolahi : Functional Dependencies: Redundancy Analysis and Correcting Violations
1. Functional Dependencies: Redundancy Analysis
and Correcting Violations
Solmaz Kolahi
solmaz@cs.ubc.ca
Postdoctoral Research Fellow
Department of Computer Science
University of British Columbia
Joint work with Leonid Libkin and Laks Lakshmanan
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 1/20
2. Motivation
Both relational and XML databases may store redundant data:
title director actor year
The Departed Scorsese DiCaprio 2006
The Departed Scorsese Nicholson 2006
Functional Dependency: Shrek the Third Miller Myers 2007
Shrek the Third Miller Murphy 2007
title → year
Shrek the Third Hui Myers 2007
Shrek the Third Hui Murphy 2007
Functional Dependency:
@AreaCode → @City
AreaCode AreaCode AreaCode AreaCode
416 416 416 416
City City City City
Toronto Toronto Toronto Toronto
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 2/20
3. Motivation
Normalization techniques try to remove redundancies:
BCNF eliminates all redundancies.
only key dependencies are allowed.
cannot always be achieved without losing dependencies.
AB → C C→B
R(A, B, C)
3NF eliminates some redundancies.
allows redundancy on prime attributes.
preserves dependencies.
XNF eliminates all redundancies w.r.t. XML functional dependencies.
only XML keys are allowed: if X → p.@l, then X → p.
introduced by Arenas & Libkin in 2002.
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 3/20
4. Motivation
Traditional normalization theory
characterizes a database as redundant or non-redundant.
does not measure redundancy.
cannot provide guidelines to reduce redundancy.
The more redundant the data, the more prone to update anomalies.
Our goal is
to show that there is a spectrum of redundancy using an
information-theoretic tool.
to choose database designs with low redundancy.
to handle databases with dependency violations.
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 4/20
5. Outline
Motivation.
Reducing redundancy in relational and XML data:
Measure of redundancy.
Redundancy analysis of normal forms and schemas.
Correcting functional dependency violations.
Conclusions.
Future work.
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 5/20
6. Measure of Information Content
Proposed by Arenas & Libkin in 2003.
Used to measure the redundancy of a data value in a database
instance with respect to a set of constraints.
Intuitively, RICI (p|Σ) measures the relative information content of
position p in instance I w.r.t. constraints Σ.
Independent of data models and query languages.
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 6/20
7. Measure of Information Content
Proposed by Arenas & Libkin in 2003.
Used to measure the redundancy of a data value in a database
instance with respect to a set of constraints.
Intuitively, RICI (p|Σ) measures the relative information content of
position p in instance I w.r.t. constraints Σ.
Independent of data models and query languages.
Σ = {A → C} A B C D
RICI (P |Σ) 1 2 3 4
0.875 1 2 3 5
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 6/20
8. Measure of Information Content
Proposed by Arenas & Libkin in 2003.
Used to measure the redundancy of a data value in a database
instance with respect to a set of constraints.
Intuitively, RICI (p|Σ) measures the relative information content of
position p in instance I w.r.t. constraints Σ.
Independent of data models and query languages.
Σ = {A → C} A B C D
RICI (P |Σ) 1 2 3 4
0.875 1 2 3 5
0.781 1 2 3 6
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 6/20
9. Measure of Information Content
Proposed by Arenas & Libkin in 2003.
Used to measure the redundancy of a data value in a database
instance with respect to a set of constraints.
Intuitively, RICI (p|Σ) measures the relative information content of
position p in instance I w.r.t. constraints Σ.
Independent of data models and query languages.
Σ = {A → C} A B C D
RICI (P |Σ) 1 2 3 4
0.875 1 2 3 5
0.781 1 2 3 6
0.711 1 2 3 7
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 6/20
10. Measure of Information Content
Proposed by Arenas & Libkin in 2003.
Used to measure the redundancy of a data value in a database
instance with respect to a set of constraints.
Intuitively, RICI (p|Σ) measures the relative information content of
position p in instance I w.r.t. constraints Σ.
Independent of data models and query languages.
Σ = {A → C} A B C D
RICI (P |Σ) 1 2 3 4
0.875 1 2 3 5
0.781 1 2 3 6
0.711 1 2 3 7
0.658 1 2 3 8
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 6/20
11. Measure of Information Content
Proposed by Arenas & Libkin in 2003.
Used to measure the redundancy of a data value in a database
instance with respect to a set of constraints.
Intuitively, RICI (p|Σ) measures the relative information content of
position p in instance I w.r.t. constraints Σ.
Independent of data models and query languages.
Σ = {A → C} Σ = {A → C, B → C}
A B C D
RICI (P |Σ) RICI (P |Σ)
1 2 3 4
0.875 0.781
1 2 3 5
0.781 0.629
1 2 3 6
0.711 0.522
1 2 3 7
0.658 0.446
1 2 3 8
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 6/20
12. Measure of Information Content
A B C
1 2 3
R(A, B, C) Σ = {A → B}
1 2 4
Pick k such that adom(I) ⊆ {1, . . . , k} (k = 7).
For every X ⊆ Pos(I) − {p} compute probability distribution P (a|X) for
every a ∈ {1, . . . , k}.
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 7/20
13. Measure of Information Content
A B C
2 3
R(A, B, C) Σ = {A → B}
1 2
Pick k such that adom(I) ⊆ {1, . . . , k} (k = 7).
For every X ⊆ Pos(I) − {p} compute probability distribution P (a|X) for
every a ∈ {1, . . . , k}.
P (2|X) =
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 7/20
14. Measure of Information Content
A B C
1 2 3
R(A, B, C) Σ = {A → B}
1 2 1
Pick k such that adom(I) ⊆ {1, . . . , k} (k = 7).
For every X ⊆ Pos(I) − {p} compute probability distribution P (a|X) for
every a ∈ {1, . . . , k}.
P (2|X) =
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 7/20
15. Measure of Information Content
A B C
4 2 3
R(A, B, C) Σ = {A → B}
1 2 7
Pick k such that adom(I) ⊆ {1, . . . , k} (k = 7).
For every X ⊆ Pos(I) − {p} compute probability distribution P (a|X) for
every a ∈ {1, . . . , k}.
P (2|X) =
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 7/20
16. Measure of Information Content
A B C
4 2 3
R(A, B, C) Σ = {A → B}
1 2 7
Pick k such that adom(I) ⊆ {1, . . . , k} (k = 7).
For every X ⊆ Pos(I) − {p} compute probability distribution P (a|X) for
every a ∈ {1, . . . , k}.
P (2|X) = 48/
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 7/20
17. Measure of Information Content
A B C
a 3
R(A, B, C) Σ = {A → B}
1 2
Pick k such that adom(I) ⊆ {1, . . . , k} (k = 7).
For every X ⊆ Pos(I) − {p} compute probability distribution P (a|X) for
every a ∈ {1, . . . , k}.
P (2|X) = 48/(48 + 6 × 42) = 0.16
P (a|X) = 42/(48 + 6 × 42) = 0.14 for every a = 2
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 7/20
18. Measure of Information Content
A B C
1 2 3
R(A, B, C) Σ = {A → B}
1 2 4
Pick k such that adom(I) ⊆ {1, . . . , k} (k = 7).
For every X ⊆ Pos(I) − {p} compute probability distribution P (a|X) for
every a ∈ {1, . . . , k}.
P (2|X) = 48/(48 + 6 × 42) = 0.16
P (a|X) = 42/(48 + 6 × 42) = 0.14 for every a = 2
Conditional entropy : 2.8057
Average over all possible X: RICk = 2.4558
I
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 7/20
19. Measure of Information Content
A B C
1 2 3
R(A, B, C) Σ = {A → B}
1 2 4
Pick k such that adom(I) ⊆ {1, . . . , k} (k = 7).
For every X ⊆ Pos(I) − {p} compute probability distribution P (a|X) for
every a ∈ {1, . . . , k}.
P (2|X) = 48/(48 + 6 × 42) = 0.16
P (a|X) = 42/(48 + 6 × 42) = 0.14 for every a = 2
Conditional entropy : 2.8057
Average over all possible X: RICk = 2.4558
I
RICk (p| Σ)
I
RICI (p|Σ) = lim = 0.875
log k
k→∞
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 7/20
20. Measure and Database Design
A schema S with constraints Σ is well-designed if for every instance I of
(S, Σ) and every position p in I RICI (p|Σ) = 1.
Known results (Arenas & Libkin, 2003):
relational databases with FDs: (S, Σ) is well-designed iff it is in BCNF.
XML documents with FDs: (S, Σ) is well-designed iff it is in XNF.
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 8/20
21. Measure and Database Design
A schema S with constraints Σ is well-designed if for every instance I of
(S, Σ) and every position p in I RICI (p|Σ) = 1.
Known results (Arenas & Libkin, 2003):
relational databases with FDs: (S, Σ) is well-designed iff it is in BCNF.
XML documents with FDs: (S, Σ) is well-designed iff it is in XNF.
Well-designed databases cannot always be achieved:
Performance issues.
Dependency preservation.
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 8/20
22. Measure and Database Design
A schema S with constraints Σ is well-designed if for every instance I of
(S, Σ) and every position p in I RICI (p|Σ) = 1.
Known results (Arenas & Libkin, 2003):
relational databases with FDs: (S, Σ) is well-designed iff it is in BCNF.
XML documents with FDs: (S, Σ) is well-designed iff it is in XNF.
Well-designed databases cannot always be achieved:
Performance issues.
Dependency preservation.
General design goal: maximizing information content to the possible
extent by enforcing some design conditions.
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 8/20
23. Guaranteed Information Content
Given a condition C, guaranteed information content (GIC) is the smallest
information content found in instances of schemas satisfying C.
instances of (S, Σ)
Schema satisfying condition C
(S, Σ) =⇒
≥ GIC
RICI (p|Σ)
More formally,
we look at the set of all possible values for information content
POSS C (m) = {RICI (p | Σ) | I is an instance of (R, Σ),
R has m attributes,
(R, Σ) satisfies C},
then GICC (m), is the infimum of POSS C (m).
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 9/20
24. Price of Dependency Preservation
Design goal: minimizing redundancy while preserving FDs.
For a normal form NF, PRICE(NF ) is the minimum information content that
NF loses to guarantee dependency preservation.
if c ∈ [0, 1] is the largest information content guaranteed for
decompositions into NF,
NF-decomposition
(R1 , Σ1 )
≥c
RICI (p|Σ)
(R, Σ) (R2 , Σ2 )
(R3 , Σ3 )
then price of dependency preservation, PRICE(NF ), is 1 − c.
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 10/20
25. Price of Dependency Preservation
Theorem
= 1/2.
PRICE(3NF)
≥ 1/2 for any dependency-preserving normal form NF.
PRICE(NF )
To pay the smallest price for achieving dependency preservation, we
should do a 3NF normalization.
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 11/20
26. Price of Dependency Preservation
Theorem
= 1/2.
PRICE(3NF)
≥ 1/2 for any dependency-preserving normal form NF.
PRICE(NF )
To pay the smallest price for achieving dependency preservation, we
should do a 3NF normalization.
Not all 3NF normalizations are equal:
Special subclasses of 3NF exist (old research).
Only one subclass (3NF+ ) achieves the smallest price.
We compare normal forms based on guaranteed information content
or highest redundancy they allow.
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 11/20
27. Comparing Normal Forms
For every m > 2:
Theorem
21−m
GICAll (m) =
22−m
GIC3NF (m) =
GIC3NF+ (m) = 1/2
3NF is twice as good as doing nothing.
3NF+ is exponentially better.
similar results obtained if we compare normal forms based on
guaranteed average information content.
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 12/20
28. Redundancy of an Arbitrary Schema
Normalizing into smaller relations is not always desirable.
losing constraints.
slowing down query answering.
Normalization decision can be made based on
how much redundancy the schema allows; or
where in the spectrum of redundancy the schema lies; or
the lowest information content found in all instances of the
schema.
Design goal: decomposing the schema with the highest potential for
redundancy.
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 13/20
29. Redundancy of an Arbitrary Schema
Given an arbitrary schema R with FDs Σ, let
Theorem
ΣA = {X | X → A, X is minimal and non-key};
#HS = the number of hitting sets of ΣA ;
l=| X|.
X∈ΣA
Then the smallest information content found in column A of instances is
GICR (A) = #HS · 2−l .
Σ
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 14/20
30. Redundancy of an Arbitrary Schema
Given an arbitrary schema R with FDs Σ, let
Theorem
ΣA = {X | X → A, X is minimal and non-key};
#HS = the number of hitting sets of ΣA ;
l=| X|.
X∈ΣA
Then the smallest information content found in column A of instances is
GICR (A) = #HS · 2−l .
Σ
R1 (A, B, C, D, E) R2 (A, B, C, D, E)
Σ1 = { AB → E, Σ2 = { BC → E,
D → E} AC → E,
BD → E}
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 14/20
31. Redundancy of an Arbitrary Schema
Given an arbitrary schema R with FDs Σ, let
Theorem
ΣA = {X | X → A, X is minimal and non-key};
#HS = the number of hitting sets of ΣA ;
l=| X|.
X∈ΣA
Then the smallest information content found in column A of instances is
GICR (A) = #HS · 2−l .
Σ
R1 (A, B, C, D, E) R2 (A, B, C, D, E)
Σ1 = { AB → E, Σ2 = { BC → E,
D → E} AC → E,
BD → E}
GICR1 (E) = GICR2 (E) =
3 1
1 2
Σ Σ
8 2
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 14/20
32. Outline
Motivation.
Reducing redundancy in relational and XML data:
Measure of redundancy.
Redundancy analysis of normal forms and schemas.
Correcting functional dependency violations.
Conclusions.
Future work.
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 15/20
33. Functional Dependency Violations
Large databases often tend to violate a set of FDs.
Σ = {cnt, arCode → reg, cnt, reg → prov }
An inconsistent database
name cnt prov reg arCode phone
t1 Smith CAN BC Van 604 123 4567
t2 Adams CAN BC Van 604 765 4321
t3 Simpson CAN BC Man 604 345 6789
t4 Rice CAN AB Vic 604 987 6543
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 16/20
34. Functional Dependency Violations
Large databases often tend to violate a set of FDs.
Σ = {cnt, arCode → reg, cnt, reg → prov }
An inconsistent database
name cnt prov reg arCode phone
t1 Smith CAN BC Van 604 123 4567
t2 Adams CAN BC Van 604 765 4321
t3 Simpson CAN BC Man 604 345 6789
t4 Rice CAN AB Vic 604 987 6543
A minimal repair
name cnt prov reg arCode phone
t1 Smith CAN BC Van 604 123 4567
t2 Adams CAN BC Van 604 765 4321
t3 Simpson CAN BC Van 604 345 6789
t4 v1
Rice AB Vic 604 987 6543
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 16/20
35. Handling Inconsistent Databases
Integrity constraints Σ (FDs, keys, etc.).
Inconsistent database D: does not satisfy Σ.
We can produce a repair R by inserting/deleting tuples or modifying
values in D.
∆(D, R) = number of modifications
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 17/20
36. Handling Inconsistent Databases
Integrity constraints Σ (FDs, keys, etc.).
Inconsistent database D: does not satisfy Σ.
We can produce a repair R by inserting/deleting tuples or modifying
values in D.
∆(D, R) = number of modifications
Handling inconsistency:
Consistent query answering:
{Q(R) | R is a minimal repair for D}
certain answer for queryQ =
Producing an optimum repair Ropt with minimum ∆.
Both approaches are intractable in general.
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 17/20
37. Handling Inconsistent Databases
Integrity constraints Σ (FDs, keys, etc.).
Inconsistent database D: does not satisfy Σ.
We can produce a repair R by inserting/deleting tuples or modifying
values in D.
∆(D, R) = number of modifications
Handling inconsistency:
Consistent query answering:
{Q(R) | R is a minimal repair for D}
certain answer for queryQ =
Producing an optimum repair Ropt with minimum ∆.
Both approaches are intractable in general.
Our approach: producing an approximate solution Rapp for optimum repair.
∆(D, Rapp ) ≤ α · ∆(D, Ropt )
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 17/20
38. Approximating Optimum Repair
Theorem. Finding an optimum solution for FD violations is NP-hard.
Theorem. Finding a constant-factor approximation for all FD violations is
NP-hard.
Theorem. For every fixed set of FDs, there is a polynomial-time algorithm
that approximates optimum repair within a factor of α, where α depends
on FDs.
A B C D E
t1 a1 b1 c1 d1 e1
t2 a2 b1 c2 d2 e2
t3 a1 b3 c3 d3 e3
t4 a4 b4 c4 d4 e4
t5 a5 b4 c5 d5 e5
t6 a6 b6 c4 d5 e6
Σ = {A → C, B → C, CD → E}
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 18/20
39. Conclusions
We analyze schemas and normal forms based on worst cases of
redundancy.
There is a spectrum of information content (redundancy) for
schemas.
0 1 1
2
poorly-designed well-designed
Producing optimum repair for FD violations is hard.
We introduced an approximation framework.
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 19/20
40. Future Work
Comparing quality of schemas with low / high information content in
practice.
Defining normalization concepts for XML such as:
dependency preserving decomposition.
Finding an equivalent of 3NF for XML as a normal form
that guarantees an information content of 1 .
2
to which every XML document is decomposable.
Extending the repair algorithm for other integrity constraints.
Solmaz Kolahi, Sharif U. of Tech., December 2008 – p. 20/20