Traditional database management systems (DBMS) are the computation
storage and reservoir of large amounts of information. The data accumulated by these
database systems is the information valid at present time, valid now. It is the data that
is true at the present moment. Past data is the information that was kept in the
database at an earlier time, data that is hold to be existed in the past, were valid at
some point before now. Future data is the information supposed to be valid at a future
time instance, data that will be true in the near future, valid at some point after now.
The commercial DBMS of today used by organizations and individuals, such as MS
SQL Server, Oracle, DB2, Sybase, Postgres etc., do not provide models to support and
process (retrieving, modifying, inserting and removing) past and future data.
The implementation of bi-temporal modelling in Microsoft SQL Server is important
to know how relational database management system handles data the bi-temporal
property. In bi-temporal database, data saved is never deleted and additional values
are always appended. Therefore, the paper explores one of the way we can build bitemporal handling of data. The paper aims to build the core concepts of bi-temporal
data storage and querying techniques used in bi-temporal relational DBMS i.e., from
data structures to normalized storage, and to extraction or slicing of data.
The unlimited growth of data results relational data to become complicated in terms
of management and storage of data. Thus, the developers working in various
commercial and industrial applications should know how bi-temporal concepts apply to relational databases, especially due to their increased flexibility in the bi-temporal
storage as well as in analyzing data. Thereby, the paper demonstrates how bi-temporal
data structures and their operations are applied in Relational Database Management
System
Management of Bi-Temporal Properties of Sql/Nosql Based Architectures – A Re...lyn kurian
Data engineering is the most important field in computer science engineering. Data is computed, stored and
manipulated according to the user requirement. Data can be text, picture, audio, video or document which is in various
formats. This paper deals with various database management systems that stores and manipulates data efficiently,
including banking system, scientific or commercial systems. Traditionally, we use RDBMS like MS SQL Server, IBM
DB2, Oracle, MySQL and Microsoft Access for transactional processing and analytical processing. On the advent of Big
Data, the strict RDBMS is not a customized solution on considering the performance and scalability aspects of the
Information Technology that needs today. The new era needs new technologies. Google introduced the concept of NoSQL
Databases in 2005, which led to the revolution of NoSQL databases like Noe4j, HBase, Redis, Mango DB etc. But
migration to NoSQL Databases is a challenging area for the database architects in various fields of business. Different
tools and techniques for database migration, query translation and query optimization is being adopted and the research
area is open. This paper comprises the categorization of the proposed and implemented bi-temporal databases along with
their bi-temporal properties till date.
An ontological approach to handle multidimensional schema evolution for data ...ijdms
In recent years, the number of digital information storage and retrieval systems has increased immensely.
Data warehousing has been found to be an extremely useful technology for integrating such heterogeneous
and autonomous information sources. Data within the data warehouse is modelled in the form of a star or
snowflake schema which facilitates business analysis in a multidimensional perspective. As user
requirements are interesting measures of business processes, the data warehouse schema is derived from
the information sources and business requirements. Due to the changing business scenario, the information
sources not only change their data, but also change their schema structure. In addition to the source
changes the business requirements for data warehouse may also change. Both these changes results in data
warehouse schema evolution. These changes can be handled either by just updating it in the DW model, or
can be developed as a new version of the DW structure. Existing approaches either deal with source
changes or requirements changes in a manual way and changes to the data warehouse schema is carried
out at the physical level. This may induce high maintenance costs and complex OLAP server
administration. As ontology seems to be a promising solution for the data warehouse research, in this
paper an ontological approach to automate the evolution of a data warehouse schema is proposed. This
method assists the data warehouse designer in handling evolution at the ontological level based on which
decision can be made to carry out the changes at the physical level. We evaluate the proposed ontological
approach with the existing method of manual adaptation of data warehouse schema.
Management of Bi-Temporal Properties of Sql/Nosql Based Architectures – A Re...lyn kurian
Data engineering is the most important field in computer science engineering. Data is computed, stored and
manipulated according to the user requirement. Data can be text, picture, audio, video or document which is in various
formats. This paper deals with various database management systems that stores and manipulates data efficiently,
including banking system, scientific or commercial systems. Traditionally, we use RDBMS like MS SQL Server, IBM
DB2, Oracle, MySQL and Microsoft Access for transactional processing and analytical processing. On the advent of Big
Data, the strict RDBMS is not a customized solution on considering the performance and scalability aspects of the
Information Technology that needs today. The new era needs new technologies. Google introduced the concept of NoSQL
Databases in 2005, which led to the revolution of NoSQL databases like Noe4j, HBase, Redis, Mango DB etc. But
migration to NoSQL Databases is a challenging area for the database architects in various fields of business. Different
tools and techniques for database migration, query translation and query optimization is being adopted and the research
area is open. This paper comprises the categorization of the proposed and implemented bi-temporal databases along with
their bi-temporal properties till date.
An ontological approach to handle multidimensional schema evolution for data ...ijdms
In recent years, the number of digital information storage and retrieval systems has increased immensely.
Data warehousing has been found to be an extremely useful technology for integrating such heterogeneous
and autonomous information sources. Data within the data warehouse is modelled in the form of a star or
snowflake schema which facilitates business analysis in a multidimensional perspective. As user
requirements are interesting measures of business processes, the data warehouse schema is derived from
the information sources and business requirements. Due to the changing business scenario, the information
sources not only change their data, but also change their schema structure. In addition to the source
changes the business requirements for data warehouse may also change. Both these changes results in data
warehouse schema evolution. These changes can be handled either by just updating it in the DW model, or
can be developed as a new version of the DW structure. Existing approaches either deal with source
changes or requirements changes in a manual way and changes to the data warehouse schema is carried
out at the physical level. This may induce high maintenance costs and complex OLAP server
administration. As ontology seems to be a promising solution for the data warehouse research, in this
paper an ontological approach to automate the evolution of a data warehouse schema is proposed. This
method assists the data warehouse designer in handling evolution at the ontological level based on which
decision can be made to carry out the changes at the physical level. We evaluate the proposed ontological
approach with the existing method of manual adaptation of data warehouse schema.
Converting UML Class Diagrams into Temporal Object Relational DataBase IJECEIAES
Number of active researchers and experts, are engaged to develop and implement new mechanism and features in time varying database management system (TVDBMS), to respond to the recommendation of modern business environment.Time-varying data management has been much taken into consideration with either the attribute or tuple time stamping schema. Our main approach here is to try to offer a better solution to all mentioned limitations of existing works, in order to provide the nonprocedural data definitions, queries of temporal data as complete as possible technical conversion ,that allow to easily realize and share all conceptual details of the UML class specifications, from conception and design point of view. This paper contributes to represent a logical design schema by UML class diagrams, which are handled by stereotypes to express a temporal object relational database with attribute timestamping.
Elimination of data redundancy before persisting into dbms using svm classifi...nalini manogaran
Elimination of data redundancy before persisting into dbms using svm classification,
Data Base Management System is one of the
growing fields in computing world. Grid computing, internet
sharing, distributed computing, parallel processing and cloud
are the areas store their huge amount of data in a DBMS to
maintain the structure of the data. Memory management is
one of the major portions in DBMS due to edit, delete, recover
and commit operations used on the records. To improve the
memory utilization efficiently, the redundant data should be
eliminated accurately. In this paper, the redundant data is
fetched by the Quick Search Bad Character (QSBC) function
and intimate to the DB admin to remove the redundancy.
QSBC function compares the entire data with patterns taken
from index table created for all the data persisted in the
DBMS to easy comparison of redundant (duplicate) data in
the database. This experiment in examined in SQL server
software on a university student database and performance is
evaluated in terms of time and accuracy. The database is
having 15000 students data involved in various activities.
Keywords—Data redundancy, Data Base Management System,
Support Vector Machine, Data Duplicate.
I. INTRODUCTION
The growing (prenominal) mass of information
present in digital media has become a resistive problem for
data administrators. Usually, shaped on data congregate
from distinct origin, data repositories such as those used by
digital libraries and e-commerce agent based records with
disparate schemata and structures. Also problems regarding
to low response time, availability, security and quality
assurance become more troublesome to manage as the
amount of data grow larger. It is practicable to specimen
that the peculiarity of the data that an association uses in its
systems is relative to its efficiency for offering beneficial
services to their users. In this environment, the
determination of maintenance repositories with “dirty” data
(i.e., with replicas, identification errors, equal patterns,
etc.) goes greatly beyond technical discussion such as the
everywhere quickness or accomplishment of data
administration systems.
Nalini.M, nalini.tptwin@gmail.com, Anbu.S, anomaly detection,
data mining
big data
dbms
intrusion detection
dublicate detection
data cleaning
data redundancy
data replication, redundancy removel, QSBC, Duplicate detection, error correction, de-duplication, Data cleaning, Dbms, Data sets
The scale of big data causes the compositions of extract-transform-load (ETL) workflows to grow increasingly complex. With the turnaround time for delivering solutions becoming a greater emphasis,
stakeholders cannot continue to afford to wait the hundreds of hours it takes for domain experts to manually compose a workflow solution. This paper describes a novel AI planning approach that facilitates rapid composition and maintenance of ETL workflows. The workflow engine is evaluated on real-world
scenarios from an industrial partner and results gathered from a prototype are reported to demonstrate the validity of the approach.
Secure Transaction Model for NoSQL Database Systems: Reviewrahulmonikasharma
NoSQL cloud database frameworks would consist new sorts of databases that would construct over many cloud hubs and would be skilled about storing and transforming enormous information. NoSQL frameworks need to be progressively utilized within substantial scale provisions that require helter skelter accessibility. What’s more effectiveness for weaker consistency? Consequently, such frameworks need help for standard transactions which give acceptable and stronger consistency. This task proposes another multi-key transactional model which gives NoSQL frameworks standard for transaction backing and stronger level from claiming information consistency. Those methodology is to supplement present NoSQL structural engineering with an additional layer that manages transactions. The recommended model may be configurable the place consistency, accessibility Furthermore effectiveness might make balanced In view of requisition prerequisites. The recommended model may be approved through a model framework utilizing MongoDB. Preliminary examinations show that it ensures stronger consistency Furthermore supports great execution.
Granularity analysis of classification and estimation for complex datasets wi...IJECEIAES
Dispersed and unstructured datasets are substantial parameters to realize an exact amount of the required space. Depending upon the size and the data distribution, especially, if the classes are significantly associating, the level of granularity to agree a precise classification of the datasets exceeds. The data complexity is one of the major attributes to govern the proper value of the granularity, as it has a direct impact on the performance. Dataset classification exhibits the vital step in complex data analytics and designs to ensure that dataset is prompt to be efficiently scrutinized. Data collections are always causing missing, noisy and out-of-the-range values. Data analytics which has not been wisely classified for problems as such can induce unreliable outcomes. Hence, classifications for complex data sources help comfort the accuracy of gathered datasets by machine learning algorithms. Dataset complexity and pre-processing time reflect the effectiveness of individual algorithm. Once the complexity of datasets is characterized then comparatively simpler datasets can further investigate with parallelism approach. Speedup performance is measured by the execution of MOA simulation. Our proposed classification approach outperforms and improves granularity level of complex datasets.
Performance evaluation and estimation model using regression method for hadoo...redpel dot com
Performance evaluation and estimation model using regression method for hadoop word count.
for more ieee paper / full abstract / implementation , just visit www.redpel.com
Second part of my tutorial (jointly with Diego Calvanese) on the "Integrated Modeling and Verification of Processes and Data", held at the BPM 2017 conference. The second part focuses on a formal, minimalistic integrated model for processes and data, namely the model of Data-Centric Dynamic Systems, and provides a comprehensive overview of the main technical results related to the boundaries of verifiability for models integrating processes and data.
Data Warehousing and Business Intelligence is one of the hottest skills today, and is the cornerstone for reporting, data science, and analytics. This course teaches the fundamentals with examples plus a project to fully illustrate the concepts.
PRIVACY PRESERVING CLUSTERING IN DATA MINING USING VQ CODE BOOK GENERATIONcscpconf
Huge Volumes of detailed personal data is regularly collected and analyzed by applications
using data mining, sharing of these data is beneficial to the application users. On one hand it is
an important asset to business organizations and governments for decision making at the same
time analysing such data opens treats to privacy if not done properly. This paper aims to reveal
the information by protecting sensitive data. We are using Vector quantization technique for
preserving privacy. Quantization will be performed on training data samples it will produce
transformed data set. This transformed data set does not reveal the original data. Hence privacy
is preserved
Transforming data-centric eXtensible markup language into relational database...journalBEEI
eXtensible markup language (XML) appeared internationally as the format for data representation over the web. Yet, most organizations are still utilising relational databases as their database solutions. As such, it is crucial to provide seamless integration via effective transformation between these database infrastructures. In this paper, we propose XML-REG to bridge these two technologies based on node-based and path-based approaches. The node-based approach is good to annotate each positional node uniquely, while the path-based approach provides summarised path information to join the nodes. On top of that, a new range labelling is also proposed to annotate nodes uniquely by ensuring the structural relationships are maintained between nodes. If a new node is to be added to the document, re-labelling is not required as the new label will be assigned to the node via the new proposed labelling scheme. Experimental evaluations indicated that the performance of XML-REG exceeded XMap, XRecursive, XAncestor and Mini-XML concerning storing time, query retrieval time and scalability. This research produces a core framework for XML to relational databases (RDB) mapping, which could be adopted in various industries.
Applications of machine learning are widely used in the real world with either supervised or unsupervised
learning process. Recently emerged domain in the information technologies is Big Data which refers to
data with characteristics such as volume, velocity and variety. The existing machine learning approaches
cannot cope with Big Data. The processing of big data has to be done in an environment where distributed
programming is supported. In such environment like Hadoop, a distributed file system like Hadoop
Distributed File System (HDFS) is required to support scalable and efficient access to data. Distributed
environments are often associated with cloud computing and data centres. Naturally such environments are
equipped with GPUs (Graphical Processing Units) that support parallel processing. Thus the environment
is suitable for processing huge amount of data in short span of time. In this paper we propose a framework
that can have generic operations that support processing of big data. Our framework provides building
blocks to support clustering of unstructured data which is in the form of documents. We proposed an
algorithm that works in scheduling jobs of multiple users. We built a prototype application to demonstrate
the proof of concept. The empirical results revealed that the proposed framework shows 95% accuracy
when the results are compared with the ground truth.
Distance based transformation for privacy preserving data mining using hybrid...csandit
Data mining techniques are used to retrieve the knowledge from large databases that helps the
organizations to establish the business effectively in the competitive world. Sometimes, it
violates privacy issues of individual customers. This paper addresses the problem of privacy
issues related to the individual customers and also propose a transformation technique based on
a Walsh-Hadamard transformation (WHT) and Rotation. The WHT generates an orthogonal
matrix, it transfers entire data into new domain but maintain the distance between the data
records these records can be reconstructed by applying statistical based techniques i.e. inverse
matrix, so this problem is resolved by applying Rotation transformation. In this work, we
increase the complexity to unauthorized persons for accessing original data of other
organizations by applying Rotation transformation. The experimental results show that, the
proposed transformation gives same classification accuracy like original data set. In this paper
we compare the results with existing techniques such as Data perturbation like Simple Additive
Noise (SAN) and Multiplicative Noise (MN), Discrete Cosine Transformation (DCT), Wavelet
and First and Second order sum and Inner product Preservation (FISIP) transformation
techniques. Based on privacy measures the paper concludes that proposed transformation
technique is better to maintain the privacy of individual customers.
Third part of my tutorial (jointly with Diego Calvanese) on the "Integrated Modeling and Verification of Processes and Data", held at the BPM 2017 conference. The third part focuses on the connection between our foundational results and concrete models, methods, and systems for the integrated management of processes and data.
Power Management in Micro grid Using Hybrid Energy Storage Systemijcnes
This paper proposed for power management in micro grid using a hybrid distributed generator based on photovoltaic, wind-driven PMDC and energy storage system is proposed. In this generator, the sources are together connected to the grid with the help of interleaved boost converter followed by an inverter. Thus, compared to earlier schemes, the proposed scheme has fewer power converters. FUZZY based MPPT controllers are also proposed for the new hybrid scheme to separately trigger the interleaved DC-DC converter and the inverter for tracking the maximum power from both the sources. The integrated operations of both the proposed controllers for different conditions are demonstrated through simulation with the help of MATLAB software
The growth of big-data sectors such as the Internet of Things (IoT) generates enormous volumes of data. As IoT devices generate a vast volume of time-series data, the Time Series Database (TSDB) popularity has grown alongside the rise of IoT. Time series databases are developed to manage and analyze huge amounts of time series data. However, it is not easy to choose the best one from them. The most popular benchmarks compare the performance of different databases to each other but use random or synthetic data that applies to only one domain. As a result, these benchmarks may not always accurately represent real-world performance. It is required to comprehensively compare the performance of time series databases with real datasets. The experiment shows significant performance differences for data injection time and query execution time when comparing real and synthetic datasets. The results are reported and analyzed.
Converting UML Class Diagrams into Temporal Object Relational DataBase IJECEIAES
Number of active researchers and experts, are engaged to develop and implement new mechanism and features in time varying database management system (TVDBMS), to respond to the recommendation of modern business environment.Time-varying data management has been much taken into consideration with either the attribute or tuple time stamping schema. Our main approach here is to try to offer a better solution to all mentioned limitations of existing works, in order to provide the nonprocedural data definitions, queries of temporal data as complete as possible technical conversion ,that allow to easily realize and share all conceptual details of the UML class specifications, from conception and design point of view. This paper contributes to represent a logical design schema by UML class diagrams, which are handled by stereotypes to express a temporal object relational database with attribute timestamping.
Elimination of data redundancy before persisting into dbms using svm classifi...nalini manogaran
Elimination of data redundancy before persisting into dbms using svm classification,
Data Base Management System is one of the
growing fields in computing world. Grid computing, internet
sharing, distributed computing, parallel processing and cloud
are the areas store their huge amount of data in a DBMS to
maintain the structure of the data. Memory management is
one of the major portions in DBMS due to edit, delete, recover
and commit operations used on the records. To improve the
memory utilization efficiently, the redundant data should be
eliminated accurately. In this paper, the redundant data is
fetched by the Quick Search Bad Character (QSBC) function
and intimate to the DB admin to remove the redundancy.
QSBC function compares the entire data with patterns taken
from index table created for all the data persisted in the
DBMS to easy comparison of redundant (duplicate) data in
the database. This experiment in examined in SQL server
software on a university student database and performance is
evaluated in terms of time and accuracy. The database is
having 15000 students data involved in various activities.
Keywords—Data redundancy, Data Base Management System,
Support Vector Machine, Data Duplicate.
I. INTRODUCTION
The growing (prenominal) mass of information
present in digital media has become a resistive problem for
data administrators. Usually, shaped on data congregate
from distinct origin, data repositories such as those used by
digital libraries and e-commerce agent based records with
disparate schemata and structures. Also problems regarding
to low response time, availability, security and quality
assurance become more troublesome to manage as the
amount of data grow larger. It is practicable to specimen
that the peculiarity of the data that an association uses in its
systems is relative to its efficiency for offering beneficial
services to their users. In this environment, the
determination of maintenance repositories with “dirty” data
(i.e., with replicas, identification errors, equal patterns,
etc.) goes greatly beyond technical discussion such as the
everywhere quickness or accomplishment of data
administration systems.
Nalini.M, nalini.tptwin@gmail.com, Anbu.S, anomaly detection,
data mining
big data
dbms
intrusion detection
dublicate detection
data cleaning
data redundancy
data replication, redundancy removel, QSBC, Duplicate detection, error correction, de-duplication, Data cleaning, Dbms, Data sets
The scale of big data causes the compositions of extract-transform-load (ETL) workflows to grow increasingly complex. With the turnaround time for delivering solutions becoming a greater emphasis,
stakeholders cannot continue to afford to wait the hundreds of hours it takes for domain experts to manually compose a workflow solution. This paper describes a novel AI planning approach that facilitates rapid composition and maintenance of ETL workflows. The workflow engine is evaluated on real-world
scenarios from an industrial partner and results gathered from a prototype are reported to demonstrate the validity of the approach.
Secure Transaction Model for NoSQL Database Systems: Reviewrahulmonikasharma
NoSQL cloud database frameworks would consist new sorts of databases that would construct over many cloud hubs and would be skilled about storing and transforming enormous information. NoSQL frameworks need to be progressively utilized within substantial scale provisions that require helter skelter accessibility. What’s more effectiveness for weaker consistency? Consequently, such frameworks need help for standard transactions which give acceptable and stronger consistency. This task proposes another multi-key transactional model which gives NoSQL frameworks standard for transaction backing and stronger level from claiming information consistency. Those methodology is to supplement present NoSQL structural engineering with an additional layer that manages transactions. The recommended model may be configurable the place consistency, accessibility Furthermore effectiveness might make balanced In view of requisition prerequisites. The recommended model may be approved through a model framework utilizing MongoDB. Preliminary examinations show that it ensures stronger consistency Furthermore supports great execution.
Granularity analysis of classification and estimation for complex datasets wi...IJECEIAES
Dispersed and unstructured datasets are substantial parameters to realize an exact amount of the required space. Depending upon the size and the data distribution, especially, if the classes are significantly associating, the level of granularity to agree a precise classification of the datasets exceeds. The data complexity is one of the major attributes to govern the proper value of the granularity, as it has a direct impact on the performance. Dataset classification exhibits the vital step in complex data analytics and designs to ensure that dataset is prompt to be efficiently scrutinized. Data collections are always causing missing, noisy and out-of-the-range values. Data analytics which has not been wisely classified for problems as such can induce unreliable outcomes. Hence, classifications for complex data sources help comfort the accuracy of gathered datasets by machine learning algorithms. Dataset complexity and pre-processing time reflect the effectiveness of individual algorithm. Once the complexity of datasets is characterized then comparatively simpler datasets can further investigate with parallelism approach. Speedup performance is measured by the execution of MOA simulation. Our proposed classification approach outperforms and improves granularity level of complex datasets.
Performance evaluation and estimation model using regression method for hadoo...redpel dot com
Performance evaluation and estimation model using regression method for hadoop word count.
for more ieee paper / full abstract / implementation , just visit www.redpel.com
Second part of my tutorial (jointly with Diego Calvanese) on the "Integrated Modeling and Verification of Processes and Data", held at the BPM 2017 conference. The second part focuses on a formal, minimalistic integrated model for processes and data, namely the model of Data-Centric Dynamic Systems, and provides a comprehensive overview of the main technical results related to the boundaries of verifiability for models integrating processes and data.
Data Warehousing and Business Intelligence is one of the hottest skills today, and is the cornerstone for reporting, data science, and analytics. This course teaches the fundamentals with examples plus a project to fully illustrate the concepts.
PRIVACY PRESERVING CLUSTERING IN DATA MINING USING VQ CODE BOOK GENERATIONcscpconf
Huge Volumes of detailed personal data is regularly collected and analyzed by applications
using data mining, sharing of these data is beneficial to the application users. On one hand it is
an important asset to business organizations and governments for decision making at the same
time analysing such data opens treats to privacy if not done properly. This paper aims to reveal
the information by protecting sensitive data. We are using Vector quantization technique for
preserving privacy. Quantization will be performed on training data samples it will produce
transformed data set. This transformed data set does not reveal the original data. Hence privacy
is preserved
Transforming data-centric eXtensible markup language into relational database...journalBEEI
eXtensible markup language (XML) appeared internationally as the format for data representation over the web. Yet, most organizations are still utilising relational databases as their database solutions. As such, it is crucial to provide seamless integration via effective transformation between these database infrastructures. In this paper, we propose XML-REG to bridge these two technologies based on node-based and path-based approaches. The node-based approach is good to annotate each positional node uniquely, while the path-based approach provides summarised path information to join the nodes. On top of that, a new range labelling is also proposed to annotate nodes uniquely by ensuring the structural relationships are maintained between nodes. If a new node is to be added to the document, re-labelling is not required as the new label will be assigned to the node via the new proposed labelling scheme. Experimental evaluations indicated that the performance of XML-REG exceeded XMap, XRecursive, XAncestor and Mini-XML concerning storing time, query retrieval time and scalability. This research produces a core framework for XML to relational databases (RDB) mapping, which could be adopted in various industries.
Applications of machine learning are widely used in the real world with either supervised or unsupervised
learning process. Recently emerged domain in the information technologies is Big Data which refers to
data with characteristics such as volume, velocity and variety. The existing machine learning approaches
cannot cope with Big Data. The processing of big data has to be done in an environment where distributed
programming is supported. In such environment like Hadoop, a distributed file system like Hadoop
Distributed File System (HDFS) is required to support scalable and efficient access to data. Distributed
environments are often associated with cloud computing and data centres. Naturally such environments are
equipped with GPUs (Graphical Processing Units) that support parallel processing. Thus the environment
is suitable for processing huge amount of data in short span of time. In this paper we propose a framework
that can have generic operations that support processing of big data. Our framework provides building
blocks to support clustering of unstructured data which is in the form of documents. We proposed an
algorithm that works in scheduling jobs of multiple users. We built a prototype application to demonstrate
the proof of concept. The empirical results revealed that the proposed framework shows 95% accuracy
when the results are compared with the ground truth.
Distance based transformation for privacy preserving data mining using hybrid...csandit
Data mining techniques are used to retrieve the knowledge from large databases that helps the
organizations to establish the business effectively in the competitive world. Sometimes, it
violates privacy issues of individual customers. This paper addresses the problem of privacy
issues related to the individual customers and also propose a transformation technique based on
a Walsh-Hadamard transformation (WHT) and Rotation. The WHT generates an orthogonal
matrix, it transfers entire data into new domain but maintain the distance between the data
records these records can be reconstructed by applying statistical based techniques i.e. inverse
matrix, so this problem is resolved by applying Rotation transformation. In this work, we
increase the complexity to unauthorized persons for accessing original data of other
organizations by applying Rotation transformation. The experimental results show that, the
proposed transformation gives same classification accuracy like original data set. In this paper
we compare the results with existing techniques such as Data perturbation like Simple Additive
Noise (SAN) and Multiplicative Noise (MN), Discrete Cosine Transformation (DCT), Wavelet
and First and Second order sum and Inner product Preservation (FISIP) transformation
techniques. Based on privacy measures the paper concludes that proposed transformation
technique is better to maintain the privacy of individual customers.
Third part of my tutorial (jointly with Diego Calvanese) on the "Integrated Modeling and Verification of Processes and Data", held at the BPM 2017 conference. The third part focuses on the connection between our foundational results and concrete models, methods, and systems for the integrated management of processes and data.
Power Management in Micro grid Using Hybrid Energy Storage Systemijcnes
This paper proposed for power management in micro grid using a hybrid distributed generator based on photovoltaic, wind-driven PMDC and energy storage system is proposed. In this generator, the sources are together connected to the grid with the help of interleaved boost converter followed by an inverter. Thus, compared to earlier schemes, the proposed scheme has fewer power converters. FUZZY based MPPT controllers are also proposed for the new hybrid scheme to separately trigger the interleaved DC-DC converter and the inverter for tracking the maximum power from both the sources. The integrated operations of both the proposed controllers for different conditions are demonstrated through simulation with the help of MATLAB software
The growth of big-data sectors such as the Internet of Things (IoT) generates enormous volumes of data. As IoT devices generate a vast volume of time-series data, the Time Series Database (TSDB) popularity has grown alongside the rise of IoT. Time series databases are developed to manage and analyze huge amounts of time series data. However, it is not easy to choose the best one from them. The most popular benchmarks compare the performance of different databases to each other but use random or synthetic data that applies to only one domain. As a result, these benchmarks may not always accurately represent real-world performance. It is required to comprehensively compare the performance of time series databases with real datasets. The experiment shows significant performance differences for data injection time and query execution time when comparing real and synthetic datasets. The results are reported and analyzed.
Growth of relational model: Interdependence and complementary to big data IJECEIAES
A database management system is a constant application of science that provides a platform for the creation, movement, and use of voluminous data. The area has witnessed a series of developments and technological advancements from its conventional structured database to the recent buzzword, bigdata. This paper aims to provide a complete model of a relational database that is still being widely used because of its well known ACID properties namely, atomicity, consistency, integrity and durability. Specifically, the objective of this paper is to highlight the adoption of relational model approaches by bigdata techniques. Towards addressing the reason for this in corporation, this paper qualitatively studied the advancements done over a while on the relational data model. First, the variations in the data storage layout are illustrated based on the needs of the application. Second, quick data retrieval techniques like indexing, query processing and concurrency control methods are revealed. The paper provides vital insights to appraise the efficiency of the structured database in the unstructured environment, particularly when both consistency and scalability become an issue in the working of the hybrid transactional and analytical database management system.
A quality of service management in distributed feedback control scheduling ar...csandit
In our days, there are many real-time applications that use data which are geographically
dispersed. The distributed real-time database management systems (DRTDBMS) have been used
to manage large amount of distributed data while meeting the stringent temporal requirements
in real-time applications. Providing Quality of Service (QoS) guarantees in DRTDBMSs is a
challenging task. To address this problem, different research works are often based on
distributed feedback control real-time scheduling architecture (DFCSA). Data replication is an
efficient method to help DRTDBMS meeting the stringent temporal requirements of real-time
applications. In literature, many works have designed algorithms that provide QoS guarantees
in distributed real-time databases using only full temporal data replication policy.
In this paper, we have applied two data replication policies in a distributed feedback control
scheduling architecture to manage a QoS performance for DRTDBMS. The first proposed data
replication policy is called semi-total replication, and the second is called partial replication
policy.
QUALITY OF SERVICE MANAGEMENT IN DISTRIBUTED FEEDBACK CONTROL SCHEDULING ARCH...cscpconf
In our days, there are many real-time applications that use data which are geographically
dispersed. The distributed real-time database management systems (DRTDBMS) have been used
to manage large amount of distributed data while meeting the stringent temporal requirements
in real-time applications. Providing Quality of Service (QoS) guarantees in DRTDBMSs is a
challenging task. To address this problem, different research works are often based on
distributed feedback control real-time scheduling architecture (DFCSA). Data replication is an
efficient method to help DRTDBMS meeting the stringent temporal requirements of real-time
applications. In literature, many works have designed algorithms that provide QoS guarantees
in distributed real-time databases using only full temporal data replication policy.
In this paper, we have applied two data replication policies in a distributed feedback control
scheduling architecture to manage a QoS performance for DRTDBMS. The first proposed data
replication policy is called semi-total replication, and the second is called partial replication
policy.
A data warehouse integrates data from various and heterogeneous data sources and creates a
consolidated view of the data that is optimized for reporting and analysis. Today, business and
technology are constantly evolving, which directly affects the data sources. New data sources can
emerge while some can become unavailable. The DW or the data mart that is based on these data
sources needs to reflect these changes. Various solutions to adapt a data warehouse after the changes
in the data sources and the business requirements have been proposed in the literature [1].
A data warehouse integrates data from various and heterogeneous data sources and creates a consolidated view of the data that is optimized for reporting and analysis. Today, business and technology are constantly evolving, which directly affects the data sources. New data sources can emerge while some can become unavailable. The DW or the data mart that is based on these data sources needs to reflect these changes. Various solutions to adapt a data warehouse after the changes in the data sources and the business requirements have been proposed in the literature [1]. However, research in the problem of DW evolution has focused mainly on managing changes in the dimensional model while other aspects related to the ETL, and maintaining the history of changes has not been addressed. The paper presents a Meta Data vault model that includes a data vault based data warehouse and a master data management. A major area of focus in this research is to keep both history of changes and a “single version of the truth,” through an MDM, integrated with the DW. The paper also outlines the load patterns used to load data into the data warehouse and materialized views to deliver data to end-users. To test the proposed model, we have used big data sets from the biomedical field and for each modification of the data source schema, we outline the changes that need to be made to the EDW, the data marts and the ETL.
A META DATA VAULT APPROACH FOR EVOLUTIONARY INTEGRATION OF BIG DATA SETS: CAS...ijcsit
A data warehouse integrates data from various and heterogeneous data sources and creates a consolidated view of the data that is optimized for reporting and analysis. Today, business and technology are constantly evolving, which directly affects the data sources. New data sources can emerge while some can become unavailable. The DW or the data mart that is based on these data sources needs to reflect these changes. Various solutions to adapt a data warehouse after the changes
in the data sources and the business requirements have been proposed in the literature [1]. However, research in the problem of DW evolution has focused mainly on managing changes in the dimensional model while other aspects related to the ETL, and maintaining the history of changes has not been addressed. The paper presents a Meta Data vault model that includes a data vault based data warehouse and a master data management. A major area of focus in this research is to keep both history of changes and a “single version of the truth,” through an MDM, integrated with the DW. The paper also outlines the load patterns used to load data into the data warehouse and materialized views to deliver data to end-users. To test the proposed model, we have used big data sets from the biomedical field and for each modification of the data source schema, we outline the changes that need to be made to the EDW, the data marts and the ETL.
A database is generally used for storing related, structured data, w.pdfangelfashions02
A database is generally used for storing related, structured data, with well defined data formats,
in an efficient manner for insert, update and/or retrieval (depending on application).
On the other hand, a file system is a more unstructured data store for storing arbitrary, probably
unrelated data. The file system is more general, and databases are built on top of the general data
storage services provided by file systems.
A Data Base Management System is a system software for easy, efficient and reliable data
processing and management. It can be used for:
Creation of a database.
Retrieval of information from the database.
Updating the database.
Managing a database.
It provides us with the many functionalities and is more advantageous than the traditional file
system in many ways listed below:
1) Processing Queries and Object Management:
In traditional file systems, we cannot store data in the form of objects. In practical-world
applications, data is stored in objects and not files. So in a file system, some application software
maps the data stored in files to objects so that can be used further.
We can directly store data in the form of objects in a database management system. Application
level code needs to be written to handle, store and scan through the data in a file system whereas
a DBMS gives us the ability to query the database.
2) Controlling redundancy and inconsistency:
Redundancy refers to repeated instances of the same data. A database system provides
redundancy control whereas in a file system, same data may be stored multiple times. For
example, if a student is studying two different educational programs in the same college, say
,Engineering and History, then his information such as the phone number and address may be
stored multiple times, once in Engineering dept and the other in History dept. Therefore, it
increases time taken to access and store data. This may also lead to inconsistent data states in
both places. A DBMS uses data normalization to avoid redundancy and duplicates.
3) Efficient memory management and indexing:
DBMS makes complex memory management easy to handle. In file systems, files are indexed in
place of objects so query operations require entire file scans whereas in a DBMS , object
indexing takes place efficiently through database schema based on any attribute of the data or a
data-property. This helps in fast retrieval of data based on the indexed attribute.
4) Concurrency control and transaction management:
Several applications allow user to simultaneously access data. This may lead to inconsistency in
data in case files are used. Consider two withdrawal transactions X and Y in which an amount of
100 and 200 is withdrawn from an account A initially containing 1000. Now since these
transactions are taking place simultaneously, different transactions may update the account
differently. X reads 1000, debits 100, updates the account A to 900, whereas X also reads 1000,
debits 200, updates A to 800. In bot.
International Journal of Engineering and Science Invention (IJESI)inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
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BI-TEMPORAL IMPLEMENTATION IN RELATIONAL DATABASE MANAGEMENT SYSTEMS: MS SQL SERVER
1. BI-TEMPORAL IMPLEMENTATION IN
RELATIONAL DATABASE
MANAGEMENT SYSTEMS: MS SQL
SERVER
Lyn Kurian1*
, Jyotsna A2
1 Rajagiri School of Engineering and
Technology (Autonomous),
Kakkanad, Ernakulam, India
Lynkurian08@gmail.com, m190311@rajagiri.edu.in
2 Rajagiri School of Engineering and
Technology (Autonomous),
Kakkanad, Ernakulam, India
jyotsnaa@rajagiritech.edu.in
Abstract. Traditional database management systems (DBMS) are the computation
storage and reservoir of large amounts of information. The data accumulated by these
database systems is the information valid at present time, valid now. It is the data that
is true at the present moment. Past data is the information that was kept in the
database at an earlier time, data that is hold to be existed in the past, were valid at
some point before now. Future data is the information supposed to be valid at a future
time instance, data that will be true in the near future, valid at some point after now.
The commercial DBMS of today used by organizations and individuals, such as MS
SQL Server, Oracle, DB2, Sybase, Postgres etc., do not provide models to support and
process (retrieving, modifying, inserting and removing) past and future data.
The implementation of bi-temporal modelling in Microsoft SQL Server is important
to know how relational database management system handles data the bi-temporal
property. In bi-temporal database, data saved is never deleted and additional values
are always appended. Therefore, the paper explores one of the way we can build bi-
temporal handling of data. The paper aims to build the core concepts of bi-temporal
data storage and querying techniques used in bi-temporal relational DBMS i.e., from
data structures to normalized storage, and to extraction or slicing of data.
The unlimited growth of data results relational data to become complicated in terms
of management and storage of data. Thus, the developers working in various
commercial and industrial applications should know how bi-temporal concepts apply
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2. to relational databases, especially due to their increased flexibility in the bi-temporal
storage as well as in analyzing data. Thereby, the paper demonstrates how bi-temporal
data structures and their operations are applied in Relational Database Management
System.
Keywords: Bi-temporal Databases, Databases, Normalization, MS SQL Server Bi-
temporal Database.
1 INTRODUCTION
The most data in systems, nowadays are temporal. The trivial task is described as tracking the
validity of an entity in a single point in time. But, bi-temporality includes another dimension
to the system. Bi-temporal model supports two different timelines - valid time and transaction
time. First one is an object’s factual time and transaction time denotes when objects are known
to the database. The notion is ’what you know’ and ’when you knew it’.
Bi-temporality is a solution in database implementation of financial institutions, trading, legal
practices or even medicine. When there is an option to return in time to an earlier period of
history, some of common complexities become of little worth. For example, in medicine, bi-
temporality can present more precise patient history. You can then correlate illnesses with pre-
existing conditions. Furthermore law practices can significantly benefit from this. Knowing
which laws where active during given time interval, it can be applied to legal artefacts.
Nevertheless, financial institutions can use bi-temporal system to comply with financial
regulations - for example, having transparent trading – i.e., uniformed, consistent data and
rewinding trading metrics. The first problem that can arise in a traditional system is scalability
issue. Conventional database management systems approach involves adding more physical
or virtual resources to the underlying server hosting the database – more CPU, more memory
or more storage i.e, designed to be vertically scaled. Such architecture creates both
technological and business barriers that will not be easy to solve. Second problem is adjusting
to modern data structures, which in itself could be a tedious task. Variety of different services
and features which not all relational databases are equipped to handle, which are leading to
diverse data structures,. In addition to this, there is one more issue, that is equally important -
speed.
Data saving or retrieving speed is significant to any database management system. The
extension of relational database management system can be an efficient solution to this when
considering the scalability and performance metrics such as speed in execution and extraction
of data in old database systems.
The paper is organized as, proposed algorithm explained in
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3. section II, model in section III, bi-temporal data definition language are presented in section
IV, bi-temporal data manipulation language in section V and Concluding remarks are given
in section VI.
2 LITREATURE REVIEW
In the thesis, “A Generalisation Approach to Temporal Data Models and their
Implementations” explains about A Temporal Relational DBMS: TimeDB. In this dissertation
[2], the author showcases that, a temporal data model has:
1. Valid time and/or transaction time as well as other time lines.
2. Temporal data structures do not make any hypothesis about a specific type system or the
presence of specific time attributes.
3. Temporal data structures has time stamping of all constructs supported by the model.
4. Temporal data structures can also overcome the vertical temporal anomaly.
5. A temporally complete query language or algebra which has snapshot reducible
semantics.
6. Temporal constraints with snapshot reducible semantics.
The bi-temporal relational DBMS, Time DB agrees the language ATSQL2. It is the result of
process of uniting three different approaches, namely
1. ATSQL2, a temporal query language on SQL.
2. Chrono Log, which introduces the concept of temporal completeness.
3. Bi-temporal ChronoSQL, featuring a bi-temporal query language.
The translation algorithm used in Time DB is, a temporal query is converted into a temporal
algebra expression using the temporal set operations union (∪ t), intersect (∩t ) and difference
(t ). Each argument to set operations is either a simple algebra expression using a temporal
select (σt ), project (πt ) and cross product (×t ) operation or the result of another temporal set
operation. Each simple algebra expression using only a combination of a select, project and
cross product operation can then be evaluated separately using a standard SELECT-FROM-
WHERE statement. These intermediate results are stored in temporary tables and then used to
calculate other parts of the expression.
In Time DB, a valid-time interval $I = [vts_#$ - vte_#$), closed at the lower bound and open
at the upper, is mapped internally to two attributes vts_#$ (valid-time start) and vte_#$ (valid
time end). Thus, each valid-time relation can have two additional (hidden) attributes.
Transaction-time tables are extended accordingly. Bitemporal tables contain attributes for
both valid time and transaction time. Bi-temporal Algebra Operations Time DB handles bi-
temporal queries. For these queries, special operations for set union, set difference, set
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4. intersection and cross product need to be implemented. A bi-temporal query is translated to
standard SQL the same way as a unitemporal query. It uses bi-temporal algebra operations
instead.
In the paper [22] Modeling and Querying Multidimensional Bitemporal Data Warehouses, by
Canan Eren Atay1, Gözde Alp2* states that a Bitemporal atom is defined as a triplet, where
the valid and transaction time components can be deduced as a time point, a time interval, or
a temporal element. A Bitemporal atom in the form of represents Valid time lower bound
(VT_LB), Valid time upper bound (VT_UB) ,Transaction time lower bound (TT_LB),
Transaction time upper bound (TT_UB) and Data value, respectively.
Richard T. Snodgrass, Michael H. Böhlen, Christian S. Jensen, and Andreas Steiner states in
their book that many applications need to keep track of the past states of the database, often
for auditing requirements. Changes are not permitted on the past states; that would disrupt
secure auditing. Instead, compensating transactions are used to correct errors. When an error
is encountered, often the analyst will look at the state of the database at a previous point in
time to determine where and how the error occurred. The integrity of the previous states of
the database is preserved, so the solution is to have the DBMS maintain transaction time
automatically.
In the paper [14] Exploiting bitemporal schema versions for managing an historical medical
data warehouse: A case study, by Maria-Trinidad SERNA-ENCINAS∗ Michel ADIBA
proposes that for the management, storage and visualization of a data warehouse with current
and historical data, in a medical environment, we propose to use bitemporal schema versions
adapted to warehouse. Nevertheless, versions management is a complex task and presents
several problems. The main problem for us is the versions explosion. Thus, we must establish
a mechanism to control this problem. Our proposition considers:
- An operator called SetVersion that allows to apply a set of changes on a version to
generate a new version.
- A set of primitives for managing schema versions and cubes, dimensions and
hierarchies.
- An evolution manager responsible for different historical schema versions
management.
- A set of integrity rules that restraint the primitive execution.
3 PROPOSED MODEL
The main goal presented here, is to create comprehensive SQL bi-temporal database. For
achieving the goal, these criteria must be met:
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5. Build bi-temporal data structure.
Investigate how to construct normalization for data and referential integrity.
Analyze bi-temporal manipulation techniques. (insertion, change, deletion)
Inspect bi-temporal data extraction techniques. (coalescing, slicing)
Outline assorted bi-temporal data extraction operations and implement them.
The solution is to have the Database Management System maintain transaction time
automatically, so that the integrity of the previous states of the database is preserved. To impart
the bi-temporal solution in MS SQL SERVER, these are the following functions to be done:
Creation of the Bi-temporal TABLE
Normalization
Insertion
Updating
Deletion
Vertical and horizontal slicing (Selection and Projection) of bi-temporal data.
3.1 MODEL
EXTENDING DATABASE
The bi-temporal database modelling is an extension of temporal database which is already
inbuilt in MS SQL Server 2016 onwards. The extending database should be predominant in
bi-temporal nature. All the constraints and rules specified for temporal database can be
incorporated into bi-temporal database. It’s crucial to be known by the developers that
focusing on bi-temporality helps in much deeper analysis for data interpretation and business
requirements than the conventional relational database.
Here, in the data model, bi-temporal interactions and statements are explained. The essential
difference comes down to storing the two time dimensions. [2] By extending non bi-temporal
or temporal database it is possible to achieve bi-temporality in MS SQL server. Due to fact
that most the database functionality can be reused, you only need to ensure data integrity and
provide proper query language.
4 Bi-temporal Data Definition Language
4.1 BI-TEMPORAL TABLE CREATION
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6. Due to the temporal upward-compatibility, temporal SQL queries returns the same results
when executed on bi-temporal tables as if they were executed on non-temporal tables. The
selection of valid data at time instant now in the tables referenced by the query and leaving
away any timestamp attributes prior to further evaluation is firstly done. A bi-temporal table
supports the tracking of a temporal table along with the time-specific data storage which is of
an application specific temporal table. Use bi-temporal tables to stay user-based period
information also as system-based historical information. Bi-temporal tables combines as
system-period temporal tables and application-period temporal tables. All the restrictions that
apply to system-period temporal tables and application temporal tables also apply to bi-
temporal tables.
The constraints induced on instituting data into a database, altering data in a database, or
removing data from a database, is such that the transformations should reflect what is
happening to data that should be happened throughout the entire lifecycle. Thus, the syntax of
those transformations maintain the semantics of the data. The constraints which every valid
database state must develop to these such as entity integrity, referential integrity, domain
integrity, and application-specific business rules, so that the data in the database is accurately
and intelligently explain what it is about to those who manipulate the database.
Statement
CREATE TABLE table name
( …. NOT NULL PRIMARY KEY CLUSTERED,
// data
….Foreign ID type REFERENCES Parent table name,
[ RowEffective StartDate ] [ date time ] NULL,
[ RowEffectiveEndDate ] [ datetime ] NULL,
[ RowAssertionStartDate ] [ datetime ]
GENERATED ALWAYS AS ROW START,
[ RowAssertionEndDate ] [ datetime ]
GENERATED ALWAYS AS ROW END,
PERIOD FOR SYSTEM TIME
([RowAssertionStartDate], [RowAssertionEndDate] ) )
WITH (SYSTEM VERSIONING = ON
(HISTORY TABLE = dbo.tablenameHistory) ) ; [12]
Normalization
Bi-temporal approach requires a modern design modelling, than the traditional design called
denormalization. Bi-temporal data has its own flaws in terms of complexity. The nature of bi-
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7. temporal data causes issues that is specific to the modelling. Typical issues are redundancy,
circumlocution, contradiction, homogeneity. As the answer, [1] 6NF allows attribute values
to change independently of each other, thus avoiding redundancy and other anomalies.
Independence can be maintained by incorporating irreducible components. When dealing with
bi-temporal data, 6NF is a wise choice but complex in the application point of view. Or
otherwise,
Time normalization
Time is distinct, and is calculated by a clock. At a certain point in time, two clocks can’t be
the same. For the time to be calculated in a database, a temporal database is required, which
stores a collection of time-related data. Such a system eases storing, retrieving and updating
historical and future data (generically referred as temporal data). Temporal data is data that
changes over time. Valid time and transaction time can be supported in together and such a
database is called a bi-temporal database, comparing to a non-temporal database. The temporal
approach initiates additional complexities, such as schema of the model design and also the
keys. It is useless if it cannot identify an application that does not evolve over time. This means
that time-varying data is necessary. Built-in time management greatly increases the
functionality of a database application. The database system thus maintains past, present, and
future versions of data. A conventional database, without temporal property, has only the most
recent data as storage. The old values are either replaced or deleted. The progress of real-world
phenomena over time cannot be stored in the database as well as accessing past data is never
considered as an option. Time values are linked with attributes that indicate their periods of
validity. Precisely, the temporal theory usually include two types of time: valid and transaction
time. Valid time is the time period during which a fact is true with reference to reality.
Transaction time is the time when a fact is recorded in the database. The concept used in time
normalization is the requirement that tuples are semantically independent of one another [3].
A relation is in time normal form (TNF) if and only if it is in Boyce-Codd-Normal Form and
there exists no temporal dependencies among its non-key attributes. [3]
i.e., the attributes should be non-synchronous in time. If they are synchronous, decompose the
table to produce the bi-temporality to each of the attribute.
5 Bi-temporal Data Manipulation Language
5.1 INSERTING DATA INTO A BI-TEMPORAL TABLE
It is same as the statement for inserting data into a temporal table.
https://doi.org/10.5281/zenodo.4696175
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8. Statement
INSERT INTO TABLE tablename
VALUES (….);
//Avoid the columns of system generated transaction time values which will be automatically
generated.
5.2 UPDATING DATA INTO A TABLE
Updating data in a bi-temporal table updates rows in history table and added to its current
table.
Statement
UPDATE table name
SET column name=’ value ’
where key=’ ’;
The updating is in such a way that the data once inputted is never gone from the database and
the data is coalesced and added to the history table along with current table.
5.3 DELETING DATA FROM A BI-TEMPORAL TABLE
Deleting data from a bi-temporal table deletes rows from the current table, and rows are added
to its associated history table and which potentially result in new rows, inserted to the bi-
temporal table.
Statement
DELETE from table WHERE...;
5.4 QUERYING BI-TEMPORAL DATA
Vertical and Horizontal Slicing i.e., querying a bi-temporal table can return results for a
specified time period. These results consists current, historic, and future data.
A new clause FOR SYSTEM TIME in the SELECT ... FROM statement, which has five new
sub clauses [10] to query bi-temporal table data:
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9. AS OF datetime
ALL
FROM start datetime TO end datetime
BETWEEN start datetime AND end datetime
CONTAINED IN (start datetime, end datetime)
SYSTEM TIME AS OF
The AS OF sub-clause returns rows from the bi-temporal and history table that are valid up to
the time specified. It gives the complete snapshot of the current values until the specified time.
SYSTEM TIME ALL
ALL returns all the rows from the current and history table.
FROM and TO clause
The sub-clause FROM-TO returns results by combing the bi-temporal and history table
because this clause excludes the upper boundary of the end time.
BETWEEN AND pair
The sub-clause BETWEEN-AND returns combined results from both the bi-temporal and
history tables. This is because this sub-clause includes the upper boundary of the end time.
CONTAINED IN
This sub clause gives all rows from the history table included in the range specified. This is
due to the command in the statement CONTAINED IN that returns data from the history table.
Statement
SELECT ∗ FROM table name FOR SYSTEM TIME ALL;
// vertical slice
SELECT* FROM table name WHERE
//horizontal slice
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10. SELECT * FROM table name
FOR SYSTEM TIME AS OF // specifying a date time − state of data
FROM TO // range − a data audit
BETWEEN AND // range − a data audit
CONTAINED IN ( , ) // range − a data audit
ALL // whole data
// vertical slice and horizontal slice i.e., bi-temporal slice
SELECT FROM table name
FOR SYSTEM TIME AS OF // specifying a date time − state of data
FROM TO // range − a data audit
BETWEEN AND // range − a data audit
CONTAINED IN ( , ) // range − a data audit
ALL // whole data
WHERE …,
6 CONCLUSION
The data in engineering is so vast that the developers should be capable of handling data
maturely. Enhanced versions of viewing and manipulating data is the need of the era. Just
holding a data is meant to be of no use. Data that is meaningfully sorted is the core of an
application. Time is the factor which produces meaning to the data. Thus, when we can add
couple of entitlements to a single data automatically motivates enhancements in the server
dimensionality.
Past historical data can always be a backbone of a business which is the complete decision
maker and the game changer. Temporality in terms of data in the present time narrows the
view of data. Enormous amount of data seen with past, present and future adds vital role to
the business. The technologies used in medicine, law, education, etc., when shown with a
meaning data brings a notion on the fact and predict the nature of the fact. There comes the
importance of bi-temporal implementation of data. The structure, the normalization, the
upward language compatibility is an essential backup solutions to the new implementation. In
spite of that, the Bi-temporal implementation can be extended to more database types -
relational, key-value stores, column value stores and document-oriented systems and graph
systems. Bi-temporal operations, such as insertion, change, deletion and slicing can be
implemented on multiple database management systems.
All core aspects of storing bi-temporal data in MS SQL Server is implemented and as more
than a model it can be a great use case in analyzing data and report generation of business
applications. This research tried to cover the bi-temporal DDL and DML operations in MS-
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11. SQL Server.
7 REFERENCES
[1] Darko Golec, Viljan Mahnič, and Tatjana Kovač, 2017. RELATIONAL MODEL OF TEMPORAL DATA
BASED ON 6TH NORMAL FORM. ISSN 1330-3651 (Print), ISSN 1848-6339(Online), Tehnički vjesnik
24, 5, 1479-1489
[2] ANDREAS STEINER, “A Generalisation Approach to Temporal Data Models and their
Implementations,” A dissertation submitted to the SWISS FEDERAL INSTITUTE OF TECHNOLOGY
ZURICH for the degree of Doctor of Technical Sciences, 1998.
[3] Tobias Schlaginhaufen, “Design and implementation of a database client application for inserting,
modifying, presentation and export of bitemporal personal data,” Diploma Thesis, April 2007
[4] Richard T. Snodgrass, 2000. Developing Time-Oriented Database Applications in SQL, Morgan
Kaufmann Publishers San Francisco, California.
[5] Tom Johnston, 2014 “Bitemporal Data Theory and Practice,” Morgan Kaufmann publications
[6] Richard T. Snodgrass , “Managing Temporal Data A Five-Part Series,” A TIMECENTER Technical
Report, September 3, 1998
[7] The Code Project Open License (CPOL) (https://www.codeproject.com/Articles/17637/Bitemporal-
Database-Table-Design-The-Basics,)
[8] A Primer on Managing Data Bitemporally (https://www.red-gate.com/simple-talk/sql/t-sql-
programming/a-primer-on-managing-data-bitemporally/)
[9] IBM
(https://www.ibm.com/support/knowledgecenter/en/SSEPGG_11.1.0/com.ibm.db2.luw.admin.dbobj.doc
/doc/t0058946.html)
[10] AmeenaLalani (https://www.mssqltips.com/sqlservertip/5436/options-to-retrieve-sql-server-temporal-
table-and-history-data/)
[11] N.Leviss (https://bitemporal.org/2018/08/07/1-overview-of-ms-sql-bitemporal-support/)
[12] Lyn Kurian, Jyotsna A, 2020 Management of Bi-Temporal Properties of Sql/Nosql Based Architectures –
A Review International Journal of Innovations in Engineering and Technology (IJIET), Volume 17 Issue
2, pp.61-67
[13] Mohammed Eshtay, Assam Sleit, Monther Aldwairi”Implementing Bi-Temporal Properties Into Various
Nosql Database Categories”, International Journal of Computing, 18(1) 2019, 45-52
[14] Maria-Trinidad SERNA-ENCINAS Michel ADIBA ”Exploiting bitemporal schema versions for
https://doi.org/10.5281/zenodo.4696175
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13. IJCSIS Call For Papers 2021-2022
https://sites.google.com/site/ijcsis/
The topics suggested by the journal can be discussed in term of concepts, state of the art, research, standards,
implementations, running experiments, applications, and industrial case studies. Authors are invited to
submit complete unpublished papers, which are not under review in any other conference or journal in
the following, but not limited to, topic areas. All tracks are open to both research and industry contributions.
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14. Expert approaches
Fuzzy algorithms
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middleware
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Technology in Education
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systems
Ubiquitous and pervasive applications
Ubiquitous Networks
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