The METL process extracts meta data from a source system, transforms it to describe a different database structure, and loads it into a target system. This allows data to be accessed across systems with different structures without changing the source. Specifically, it extracts meta data from a legacy banking system, transforms it to work with a business intelligence tool, and loads it so the tool can query the legacy data through an SQL interface without knowing the source's structure. The METL process facilitates data sharing across different systems in a non-invasive way to prolong legacy systems' lives and provide access to production data.
This document discusses a technique called Keysum for generating unique keys for rows in databases. Keysum involves taking the checksum of the string that makes up the primary key for a row. This generates a large integer that serves as a unique identifier for indexing and joining rows. Checksums like CRC32 and MD5 are recommended to generate the keys. While checksums are not guaranteed to be unique, they significantly reduce the chances of duplicates compared to traditional string keys and allow data to be efficiently indexed and validated when reloaded.
Those responsible for data management often struggle due to the many responsibilities involved. While organizations recognize data as a key asset, they are often unable to properly manage it. Creating a "Literal Staging Area" or LSA platform can help take a holistic view of improving overall data management. An LSA makes a copy of business systems that is refreshed daily and can be used for tasks like data quality monitoring, analysis, and operational reporting to help address data management challenges in a cost effective way for approximately $120,000.
Logical replication allows migration between different hardware, operating systems, and Oracle versions with minimal downtime. It works by reading the redo logs of the source database in real time and applying the changes to the target database. Some preparation is required, such as testing and validating the migration. If issues occur during cutover to the 12c target, the original production system remains intact with no data risk. Logical replication provides an effective method for migrating to Oracle 12c with zero or near-zero downtime.
Big Data Taiwan 2014 Keynote 4: Monetize Enterprise Data – Big Data 在台灣的經典應用與行動Etu Solution
講者:Etu 資深協理 | 陳育杰
簡介:過去這兩年內,Big Data 在企業的應用架構已逐漸形塑出來,我們看到,不同的產業,陸續開始運用 Hadoop 來解決不同的問題,而背後的 IT 架構,其實都具有一些共通性。我們將透過這些共通性的架構來探索 Big Data / Hadoop 具體展現的企業應用。
Lecture 03 - The Data Warehouse and Design phanleson
The chapter discusses the design process for building a data warehouse, including designing the interface from operational systems and designing the data warehouse itself. It covers topics like beginning with operational data, data and process models, data warehouse data models at different levels, normalization and denormalization, and managing complexity in transformations and integrations between operational and warehouse systems. The goal is to extract, transform, and load relevant data from operational sources into the data warehouse in a way that supports analysis and decision-making.
Big data processing using HPCC Systems Above and Beyond HadoopHPCC Systems
Presentation delivered at Boston Data Festival September 2015. Data-intensive computing represents a new computing paradigm to address Big Data processing requirements using high-performance architectures supporting scalable parallel processing to allow government, commercial organizations, and research environments to process massive amounts of data and implement new applications previously thought to be impractical or infeasible. The fundamental challenges of data-intensive computing are managing and processing exponentially growing data volumes, significantly reducing associated data analysis cycles to support practical, timely applications, and developing new algorithms which can scale to search and process massive amounts of data. The open source HPCC (High-Performance Computing Cluster) Systems platform offers a unified approach to Big Data processing requirements: (1) a scalable, integrated computer systems hardware and software architecture designed for parallel processing of data-intensive computing applications, and (2) a new programming paradigm in the form of a high-level, declarative, data-centric programming language designed specifically for big data processing. This presentation explores the challenges of data-intensive computing from a programming perspective, and describes the ECL programming language and the HPCC architecture designed for data-intensive computing applications. HPCC is an alternative to the Hadoop platform, and ECL is compared to Pig Latin, a high-level language developed for the Hadoop MapReduce architecture.
This document discusses data extraction and transformation in an ETL process. It covers extracting changed data from modern systems using techniques like timestamps and triggers, as well as extracting from legacy systems using log tapes. The document also discusses major types of transformations including format revision, merging information, and date/time conversion. Finally, it provides examples of data content defects seen in source systems.
The METL process extracts meta data from a source system, transforms it to describe a different database structure, and loads it into a target system. This allows data to be accessed across systems with different structures without changing the source. Specifically, it extracts meta data from a legacy banking system, transforms it to work with a business intelligence tool, and loads it so the tool can query the legacy data through an SQL interface without knowing the source's structure. The METL process facilitates data sharing across different systems in a non-invasive way to prolong legacy systems' lives and provide access to production data.
This document discusses a technique called Keysum for generating unique keys for rows in databases. Keysum involves taking the checksum of the string that makes up the primary key for a row. This generates a large integer that serves as a unique identifier for indexing and joining rows. Checksums like CRC32 and MD5 are recommended to generate the keys. While checksums are not guaranteed to be unique, they significantly reduce the chances of duplicates compared to traditional string keys and allow data to be efficiently indexed and validated when reloaded.
Those responsible for data management often struggle due to the many responsibilities involved. While organizations recognize data as a key asset, they are often unable to properly manage it. Creating a "Literal Staging Area" or LSA platform can help take a holistic view of improving overall data management. An LSA makes a copy of business systems that is refreshed daily and can be used for tasks like data quality monitoring, analysis, and operational reporting to help address data management challenges in a cost effective way for approximately $120,000.
Logical replication allows migration between different hardware, operating systems, and Oracle versions with minimal downtime. It works by reading the redo logs of the source database in real time and applying the changes to the target database. Some preparation is required, such as testing and validating the migration. If issues occur during cutover to the 12c target, the original production system remains intact with no data risk. Logical replication provides an effective method for migrating to Oracle 12c with zero or near-zero downtime.
Big Data Taiwan 2014 Keynote 4: Monetize Enterprise Data – Big Data 在台灣的經典應用與行動Etu Solution
講者:Etu 資深協理 | 陳育杰
簡介:過去這兩年內,Big Data 在企業的應用架構已逐漸形塑出來,我們看到,不同的產業,陸續開始運用 Hadoop 來解決不同的問題,而背後的 IT 架構,其實都具有一些共通性。我們將透過這些共通性的架構來探索 Big Data / Hadoop 具體展現的企業應用。
Lecture 03 - The Data Warehouse and Design phanleson
The chapter discusses the design process for building a data warehouse, including designing the interface from operational systems and designing the data warehouse itself. It covers topics like beginning with operational data, data and process models, data warehouse data models at different levels, normalization and denormalization, and managing complexity in transformations and integrations between operational and warehouse systems. The goal is to extract, transform, and load relevant data from operational sources into the data warehouse in a way that supports analysis and decision-making.
Big data processing using HPCC Systems Above and Beyond HadoopHPCC Systems
Presentation delivered at Boston Data Festival September 2015. Data-intensive computing represents a new computing paradigm to address Big Data processing requirements using high-performance architectures supporting scalable parallel processing to allow government, commercial organizations, and research environments to process massive amounts of data and implement new applications previously thought to be impractical or infeasible. The fundamental challenges of data-intensive computing are managing and processing exponentially growing data volumes, significantly reducing associated data analysis cycles to support practical, timely applications, and developing new algorithms which can scale to search and process massive amounts of data. The open source HPCC (High-Performance Computing Cluster) Systems platform offers a unified approach to Big Data processing requirements: (1) a scalable, integrated computer systems hardware and software architecture designed for parallel processing of data-intensive computing applications, and (2) a new programming paradigm in the form of a high-level, declarative, data-centric programming language designed specifically for big data processing. This presentation explores the challenges of data-intensive computing from a programming perspective, and describes the ECL programming language and the HPCC architecture designed for data-intensive computing applications. HPCC is an alternative to the Hadoop platform, and ECL is compared to Pig Latin, a high-level language developed for the Hadoop MapReduce architecture.
This document discusses data extraction and transformation in an ETL process. It covers extracting changed data from modern systems using techniques like timestamps and triggers, as well as extracting from legacy systems using log tapes. The document also discusses major types of transformations including format revision, merging information, and date/time conversion. Finally, it provides examples of data content defects seen in source systems.
IRJET- A Study of Privacy Preserving Data Mining and TechniquesIRJET Journal
This document summarizes a study on privacy preserving data mining techniques. It begins with an abstract that introduces privacy preserving data mining as a technique for analyzing shared data while preserving data sensitivity and privacy. It then reviews literature on recent privacy preserving data mining techniques, including techniques for vertically partitioned databases using homomorphic encryption. The document proposes a new privacy preserving association rule mining model and technique. It concludes that privacy preserving data mining is an important new technique for situations where different parties need to combine data for analysis while preserving privacy.
HPCC Systems - Open source, Big Data Processing & AnalyticsHPCC Systems
This document summarizes HPCC Systems, an open source big data processing and analytics platform. It provides high-performance computing capabilities to integrate vast amounts of data from multiple sources and enable real-time queries and analysis. The platform uses the ECL programming language which allows for declarative, implicitly parallel programming optimized for data-intensive applications. It also describes LexisNexis' use of HPCC Systems and related technologies like SALT and LexID to link and analyze large datasets to derive insights for risk assessment and fraud detection across various industries.
A Comprehensive Study on Big Data Applications and Challengesijcisjournal
Big Data has gained much interest from the academia and the IT industry. In the digital and computing
world, information is generated and collected at a rate that quickly exceeds the boundary range. As
information is transferred and shared at light speed on optic fiber and wireless networks, the volume of
data and the speed of market growth increase. Conversely, the fast growth rate of such large data
generates copious challenges, such as the rapid growth of data, transfer speed, diverse data, and security.
Even so, Big Data is still in its early stage, and the domain has not been reviewed in general. Hence, this
study expansively surveys and classifies an assortment of attributes of Big Data, including its nature,
definitions, rapid growth rate, volume, management, analysis, and security. This study also proposes a
data life cycle that uses the technologies and terminologies of Big Data. Map/Reduce is a programming
model for efficient distributed computing. It works well with semi-structured and unstructured data. A
simple model but good for a lot of applications like Log processing and Web index building.
Introduction to Big Data An analogy between Sugar Cane & Big DataJean-Marc Desvaux
Big data is large and complex data that exceeds the processing capacity of conventional database systems. It is characterized by high volume, velocity, and variety of data. An enterprise can leverage big data through an analytical use to gain new insights, or through enabling new data-driven products and services. An analogy compares an enterprise's big data architecture to a sugar cane factory that acquires, organizes, analyzes, and generates business intelligence from big data sources to create value for the organization. NoSQL databases are complementary to rather than replacements for relational databases in big data solutions.
A Comparison of EDB Postgres to Self-Supported PostgreSQLEDB
This document compares using the EDB Postgres Platform versus self-supporting PostgreSQL. The EDB Postgres Platform provides increased security features like enhanced auditing, password policy management, and SQL injection protection. It also includes enterprise-ready management tools for high availability, backup/recovery, replication, monitoring and tuning that are integrated and tested. In contrast, self-supporting PostgreSQL users must evaluate and manage multiple independent open source projects for these capabilities.
The document describes the Data Vault modeling technique which involves storing historical data from multiple sources in a series of normalized tables. It outlines the key components of a Data Vault including hubs, links, and satellites. It then discusses how to implement a Data Vault using an ETL framework, metadata tables, and automation to load the Data Vault from source systems in a standardized, repeatable process.
This document provides an overview of Oracle 11g data warehousing capabilities. It discusses key concepts like what a data warehouse is and its characteristics. It also outlines the common Oracle data warehousing tasks and steps for setting up a data warehouse system, including preparing the environment, configuring the database, and accessing Oracle Warehouse Builder.
Just the sketch: advanced streaming analytics in Apache MetronDataWorks Summit
Doing advanced analytics in streaming architectures presents unique challenges around the tradeoff of having more context vs. performance. Typically performance and scalability requirements mandate that each message in a stream be operated on without the context of other messages in the stream that may have come before. In this talk, we will talk about using sketching algorithms to engineering a compromise which allows us to consider historical state without compromising scalability.
What we found analyzing the capabilities of many similar SIEMs and cybersecurity platforms is that a good portion of the advanced anaytics boil down to either simple rules enriched with the ability to do statistical baselining, set existence, and set cardinality computations. These operations are necessarily difficult to do in-stream, so often they're done after the fact. We look at ways to open up these analytics to stream computation without sacrificing scalability.
Specifically, we will introduce the infrastructure built for Apache Metron to perform these kinds of tasks. We will cover the novel integration between an Apache Storm and Apache Hbase and orchestrated by a custom domain specific language called Stellar to take all the sting out of constructing sketches and using them to accomplish simple and more advanced analytics such as statistical outlier analysis in stream. CASEY STELLA, Principal Software Engineer, Hortonworks
Gartner magic quadrant for data warehouse database management systemsparamitap
The document provides an overview and analysis of various data warehouse database management systems. It begins with definitions of key terms and an explanation of the research methodology. The bulk of the document consists of individual vendor summaries that identify strengths and cautions for each vendor based on Gartner's research. Major vendors discussed include Amazon Web Services, Cloudera, IBM, Microsoft, Oracle, SAP, Teradata and others.
Hitachi Data Systems provides storage solutions to help life sciences organizations address challenges from rapidly growing data volumes. Their solutions offer automated data migration between storage tiers to optimize storage utilization and improve computational workflow performance. Long-term data management needs are met through integrated archiving functionality allowing long-term retention of data as required by regulations.
A cyber physical stream algorithm for intelligent software defined storageMade Artha
The document presents a new Cyber Physical Stream (CPS) algorithm for selecting predominant items from large data streams. The algorithm works well for item frequencies starting from 2%. It is designed for use in intelligent Software-Defined Storage systems combined with fuzzy indexing. Experiments show CPS improves accuracy and efficiency over previous algorithms. CPS is inspired by a brain model and works by incrementing a "voltage" value when items match and decrementing it otherwise, selecting the item with highest voltage. It performs well on both uniform random and Zipf's law distributed streams, with optimal parameter values depending on the distribution.
William Inmon is considered the father of data warehousing. He has over 35 years of experience in database technology and data warehouse design. Inmon has written over 650 articles and published 45 books on topics related to building, using, and maintaining data warehouses and information factories. A data warehouse is a collection of integrated, subject-oriented databases designed to support decision-making. It contains data that is non-volatile, time-variant, integrated, and summarized for analysis. Key components of a data warehouse environment include the data store, data marts, and metadata.
This document discusses the evolution from traditional RDBMS to big data analytics. As data volumes grow rapidly, traditional RDBMS struggle to store and process large amounts of data. Hadoop provides a framework to store and process big data across commodity hardware. Key components of Hadoop include HDFS for distributed storage, MapReduce for distributed processing, Hive for SQL-like queries, and Sqoop for transferring data between Hadoop and relational databases. The document also outlines some applications and limitations of Hadoop.
Benefits of data_archiving_in_data _warehousesSurendar Bandi
This document discusses the benefits of using data archiving to manage rapid data growth in data warehouses. Some key points:
- Data warehouses often experience rapid data growth from factors like expanding subject areas, business growth, and a lack of data retention policies. This unchecked growth leads to increasing costs, poor performance, and an inability to support compliance requirements.
- Traditional solutions like hardware upgrades, backups, and database partitioning do not effectively address the problems caused by rapid data growth.
- Data archiving allows organizations to intelligently move inactive and historical data from the production database to more cost-effective storage while still providing query access. This improves performance, reduces costs, and helps manage compliance requirements.
Optimising Data Lakes for Financial ServicesAndrew Carr
By using a data lake, you can potentially do more with your company’s data than ever before.
You can gather insights by combining previously disparate data sets, optimise your operations, and build new products. However, how you design the architecture and implementation can significantly impact the results. In this white paper, we propose a number of ways to tackle such challenges and optimise the data lake to ensure it fulfils its desired function.
Data Deduplication: Venti and its improvementsUmair Amjad
This document summarizes the data deduplication system called Venti and improvements over it. Venti identifies duplicate data blocks using cryptographic hashes of block contents. It stores only a single copy of each unique block. The document discusses three key limitations of Venti: hash collisions, fixed-size chunking sensitivity, and access control. It then summarizes approaches taken by other systems to improve on these limitations, such as using multiple hash functions to reduce collisions, variable-length chunking, and stronger authentication and encryption. In conclusion, while Venti was effective at eliminating data duplication, later systems aimed to address its remaining challenges to handle growing archive sizes securely and efficiently.
Hari Arjun Duche has over 12 years of experience working with companies like Persistent Systems and IBM India Software Labs. He has extensive experience in database internals, data warehousing, and business intelligence. Some of his areas of expertise include RDBMS like Netezza and PostgreSQL, programming languages like C/C++, and tools like GDB. He has worked on projects involving data engine design, data migration, performance improvement, and developing new product features. Hari has published 4 patents and received several awards for his work.
The Data Warehouse is a database which merges, summarizes and analyzes all data sources of a company/organization. Users can request particular data from the system (such the number of sales within a certain period) and will be provided with the respective information.
With the help of the Data Warehouse, you can quickly access different systems and look at historic data. Due to the vast amount of data it provides, the Data Warehouse is an essential tool when making management decisions.
This document summarizes a study on the role of Hadoop in information technology. It discusses how Hadoop provides a flexible and scalable architecture for processing large datasets in a distributed manner across commodity hardware. It overcomes limitations of traditional data analytics architectures that could only analyze a small percentage of data due to restrictions in data storage and retrieval speeds. Key features of Hadoop include being economical, scalable, flexible and reliable for storing and processing large amounts of both structured and unstructured data from multiple sources in a fault-tolerant manner.
The hidden engineering behind machine learning products at HelixaAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
The hidden engineering behind machine learning products at Helixa
Gianmario Spacagna, (Helixa)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
On July, 20th, 2010 IBM announced the IBM TS7610 ProtecTIER® Deduplication Appliance Express, a complete deduplicated storage subsystem for Small Medium Enterprises (SMEs) and remote offices. The new subsystem is the newest and smallest member of the ProtecTIER series–a leading enterprise-suitable deduplication technology, which IBM acquired from Diligent Technologies in 2008 and continues to develop and enhance at a remarkable pace. The TS7610 uses the same ProtecTIER software found in their larger TS7650 solutions, has the same ProtecTIER functionality, is pre-configured (ready to use) and offers very competitive CapEx and OpEx pricing. Learn More: http://ibm.co/ONeH7m
Enterprise Storage Solutions for Overcoming Big Data and Analytics ChallengesINFINIDAT
Big Data and analytics workloads represent a new frontier for organizations. Data is being collected from sources that did not exist 10 years ago. Mobile phone data, machine-generated data, and website interaction data are all being collected and analyzed. In addition, as IT budgets are already being pressured down, Big Data footprints are getting larger and posing a huge storage challenge.
This paper provides information on the issues that Big Data applications pose for storage systems and how choosing the correct storage infrastructure can streamline and consolidate Big Data and analytics applications without breaking the bank.
InfiniBox bridges the gap between high performance and high capacity for Big Data applications. InfiniBox allows an organization implementing Big Data and Analytics projects to truly attain its business goals: cost reduction, continual and deep capacity scaling, and simple and effective management — and without any compromises in performance or reliability. All of this to effectively and efficiently support Big Data applications at a disruptive price point.
Learn more at www.infinidat.com.
IRJET- A Study of Privacy Preserving Data Mining and TechniquesIRJET Journal
This document summarizes a study on privacy preserving data mining techniques. It begins with an abstract that introduces privacy preserving data mining as a technique for analyzing shared data while preserving data sensitivity and privacy. It then reviews literature on recent privacy preserving data mining techniques, including techniques for vertically partitioned databases using homomorphic encryption. The document proposes a new privacy preserving association rule mining model and technique. It concludes that privacy preserving data mining is an important new technique for situations where different parties need to combine data for analysis while preserving privacy.
HPCC Systems - Open source, Big Data Processing & AnalyticsHPCC Systems
This document summarizes HPCC Systems, an open source big data processing and analytics platform. It provides high-performance computing capabilities to integrate vast amounts of data from multiple sources and enable real-time queries and analysis. The platform uses the ECL programming language which allows for declarative, implicitly parallel programming optimized for data-intensive applications. It also describes LexisNexis' use of HPCC Systems and related technologies like SALT and LexID to link and analyze large datasets to derive insights for risk assessment and fraud detection across various industries.
A Comprehensive Study on Big Data Applications and Challengesijcisjournal
Big Data has gained much interest from the academia and the IT industry. In the digital and computing
world, information is generated and collected at a rate that quickly exceeds the boundary range. As
information is transferred and shared at light speed on optic fiber and wireless networks, the volume of
data and the speed of market growth increase. Conversely, the fast growth rate of such large data
generates copious challenges, such as the rapid growth of data, transfer speed, diverse data, and security.
Even so, Big Data is still in its early stage, and the domain has not been reviewed in general. Hence, this
study expansively surveys and classifies an assortment of attributes of Big Data, including its nature,
definitions, rapid growth rate, volume, management, analysis, and security. This study also proposes a
data life cycle that uses the technologies and terminologies of Big Data. Map/Reduce is a programming
model for efficient distributed computing. It works well with semi-structured and unstructured data. A
simple model but good for a lot of applications like Log processing and Web index building.
Introduction to Big Data An analogy between Sugar Cane & Big DataJean-Marc Desvaux
Big data is large and complex data that exceeds the processing capacity of conventional database systems. It is characterized by high volume, velocity, and variety of data. An enterprise can leverage big data through an analytical use to gain new insights, or through enabling new data-driven products and services. An analogy compares an enterprise's big data architecture to a sugar cane factory that acquires, organizes, analyzes, and generates business intelligence from big data sources to create value for the organization. NoSQL databases are complementary to rather than replacements for relational databases in big data solutions.
A Comparison of EDB Postgres to Self-Supported PostgreSQLEDB
This document compares using the EDB Postgres Platform versus self-supporting PostgreSQL. The EDB Postgres Platform provides increased security features like enhanced auditing, password policy management, and SQL injection protection. It also includes enterprise-ready management tools for high availability, backup/recovery, replication, monitoring and tuning that are integrated and tested. In contrast, self-supporting PostgreSQL users must evaluate and manage multiple independent open source projects for these capabilities.
The document describes the Data Vault modeling technique which involves storing historical data from multiple sources in a series of normalized tables. It outlines the key components of a Data Vault including hubs, links, and satellites. It then discusses how to implement a Data Vault using an ETL framework, metadata tables, and automation to load the Data Vault from source systems in a standardized, repeatable process.
This document provides an overview of Oracle 11g data warehousing capabilities. It discusses key concepts like what a data warehouse is and its characteristics. It also outlines the common Oracle data warehousing tasks and steps for setting up a data warehouse system, including preparing the environment, configuring the database, and accessing Oracle Warehouse Builder.
Just the sketch: advanced streaming analytics in Apache MetronDataWorks Summit
Doing advanced analytics in streaming architectures presents unique challenges around the tradeoff of having more context vs. performance. Typically performance and scalability requirements mandate that each message in a stream be operated on without the context of other messages in the stream that may have come before. In this talk, we will talk about using sketching algorithms to engineering a compromise which allows us to consider historical state without compromising scalability.
What we found analyzing the capabilities of many similar SIEMs and cybersecurity platforms is that a good portion of the advanced anaytics boil down to either simple rules enriched with the ability to do statistical baselining, set existence, and set cardinality computations. These operations are necessarily difficult to do in-stream, so often they're done after the fact. We look at ways to open up these analytics to stream computation without sacrificing scalability.
Specifically, we will introduce the infrastructure built for Apache Metron to perform these kinds of tasks. We will cover the novel integration between an Apache Storm and Apache Hbase and orchestrated by a custom domain specific language called Stellar to take all the sting out of constructing sketches and using them to accomplish simple and more advanced analytics such as statistical outlier analysis in stream. CASEY STELLA, Principal Software Engineer, Hortonworks
Gartner magic quadrant for data warehouse database management systemsparamitap
The document provides an overview and analysis of various data warehouse database management systems. It begins with definitions of key terms and an explanation of the research methodology. The bulk of the document consists of individual vendor summaries that identify strengths and cautions for each vendor based on Gartner's research. Major vendors discussed include Amazon Web Services, Cloudera, IBM, Microsoft, Oracle, SAP, Teradata and others.
Hitachi Data Systems provides storage solutions to help life sciences organizations address challenges from rapidly growing data volumes. Their solutions offer automated data migration between storage tiers to optimize storage utilization and improve computational workflow performance. Long-term data management needs are met through integrated archiving functionality allowing long-term retention of data as required by regulations.
A cyber physical stream algorithm for intelligent software defined storageMade Artha
The document presents a new Cyber Physical Stream (CPS) algorithm for selecting predominant items from large data streams. The algorithm works well for item frequencies starting from 2%. It is designed for use in intelligent Software-Defined Storage systems combined with fuzzy indexing. Experiments show CPS improves accuracy and efficiency over previous algorithms. CPS is inspired by a brain model and works by incrementing a "voltage" value when items match and decrementing it otherwise, selecting the item with highest voltage. It performs well on both uniform random and Zipf's law distributed streams, with optimal parameter values depending on the distribution.
William Inmon is considered the father of data warehousing. He has over 35 years of experience in database technology and data warehouse design. Inmon has written over 650 articles and published 45 books on topics related to building, using, and maintaining data warehouses and information factories. A data warehouse is a collection of integrated, subject-oriented databases designed to support decision-making. It contains data that is non-volatile, time-variant, integrated, and summarized for analysis. Key components of a data warehouse environment include the data store, data marts, and metadata.
This document discusses the evolution from traditional RDBMS to big data analytics. As data volumes grow rapidly, traditional RDBMS struggle to store and process large amounts of data. Hadoop provides a framework to store and process big data across commodity hardware. Key components of Hadoop include HDFS for distributed storage, MapReduce for distributed processing, Hive for SQL-like queries, and Sqoop for transferring data between Hadoop and relational databases. The document also outlines some applications and limitations of Hadoop.
Benefits of data_archiving_in_data _warehousesSurendar Bandi
This document discusses the benefits of using data archiving to manage rapid data growth in data warehouses. Some key points:
- Data warehouses often experience rapid data growth from factors like expanding subject areas, business growth, and a lack of data retention policies. This unchecked growth leads to increasing costs, poor performance, and an inability to support compliance requirements.
- Traditional solutions like hardware upgrades, backups, and database partitioning do not effectively address the problems caused by rapid data growth.
- Data archiving allows organizations to intelligently move inactive and historical data from the production database to more cost-effective storage while still providing query access. This improves performance, reduces costs, and helps manage compliance requirements.
Optimising Data Lakes for Financial ServicesAndrew Carr
By using a data lake, you can potentially do more with your company’s data than ever before.
You can gather insights by combining previously disparate data sets, optimise your operations, and build new products. However, how you design the architecture and implementation can significantly impact the results. In this white paper, we propose a number of ways to tackle such challenges and optimise the data lake to ensure it fulfils its desired function.
Data Deduplication: Venti and its improvementsUmair Amjad
This document summarizes the data deduplication system called Venti and improvements over it. Venti identifies duplicate data blocks using cryptographic hashes of block contents. It stores only a single copy of each unique block. The document discusses three key limitations of Venti: hash collisions, fixed-size chunking sensitivity, and access control. It then summarizes approaches taken by other systems to improve on these limitations, such as using multiple hash functions to reduce collisions, variable-length chunking, and stronger authentication and encryption. In conclusion, while Venti was effective at eliminating data duplication, later systems aimed to address its remaining challenges to handle growing archive sizes securely and efficiently.
Hari Arjun Duche has over 12 years of experience working with companies like Persistent Systems and IBM India Software Labs. He has extensive experience in database internals, data warehousing, and business intelligence. Some of his areas of expertise include RDBMS like Netezza and PostgreSQL, programming languages like C/C++, and tools like GDB. He has worked on projects involving data engine design, data migration, performance improvement, and developing new product features. Hari has published 4 patents and received several awards for his work.
The Data Warehouse is a database which merges, summarizes and analyzes all data sources of a company/organization. Users can request particular data from the system (such the number of sales within a certain period) and will be provided with the respective information.
With the help of the Data Warehouse, you can quickly access different systems and look at historic data. Due to the vast amount of data it provides, the Data Warehouse is an essential tool when making management decisions.
This document summarizes a study on the role of Hadoop in information technology. It discusses how Hadoop provides a flexible and scalable architecture for processing large datasets in a distributed manner across commodity hardware. It overcomes limitations of traditional data analytics architectures that could only analyze a small percentage of data due to restrictions in data storage and retrieval speeds. Key features of Hadoop include being economical, scalable, flexible and reliable for storing and processing large amounts of both structured and unstructured data from multiple sources in a fault-tolerant manner.
The hidden engineering behind machine learning products at HelixaAlluxio, Inc.
Data Orchestration Summit 2020 organized by Alluxio
https://www.alluxio.io/data-orchestration-summit-2020/
The hidden engineering behind machine learning products at Helixa
Gianmario Spacagna, (Helixa)
About Alluxio: alluxio.io
Engage with the open source community on slack: alluxio.io/slack
On July, 20th, 2010 IBM announced the IBM TS7610 ProtecTIER® Deduplication Appliance Express, a complete deduplicated storage subsystem for Small Medium Enterprises (SMEs) and remote offices. The new subsystem is the newest and smallest member of the ProtecTIER series–a leading enterprise-suitable deduplication technology, which IBM acquired from Diligent Technologies in 2008 and continues to develop and enhance at a remarkable pace. The TS7610 uses the same ProtecTIER software found in their larger TS7650 solutions, has the same ProtecTIER functionality, is pre-configured (ready to use) and offers very competitive CapEx and OpEx pricing. Learn More: http://ibm.co/ONeH7m
Enterprise Storage Solutions for Overcoming Big Data and Analytics ChallengesINFINIDAT
Big Data and analytics workloads represent a new frontier for organizations. Data is being collected from sources that did not exist 10 years ago. Mobile phone data, machine-generated data, and website interaction data are all being collected and analyzed. In addition, as IT budgets are already being pressured down, Big Data footprints are getting larger and posing a huge storage challenge.
This paper provides information on the issues that Big Data applications pose for storage systems and how choosing the correct storage infrastructure can streamline and consolidate Big Data and analytics applications without breaking the bank.
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Entity resolution for hierarchical data using attributes value comparison ove...IAEME Publication
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Sintelix Software is Fantastic For Text Mining Software
1. Sintelix Software is Fantastic For Text Mining Software
At Semantic Sciences we have functioned to give the best entity extractor on the marketplace. Our
clients inform us that we have prospered.
The five locations of performance in which we attempt to make Sintelix stand out are:.
body acknowledgment precision (preciseness, recall, F1, F2),.
paper handling speed,.
search rate,.
equipment footprint, and.
ease of use of the icon and the system's combination user interfaces.
Entity and Partnership Acknowledgment Accuracy.
A snapshot of the Sintelix's entity recognition performance is received the table listed below. It
reveals credit scores and direct matters of outcomes calculated utilizing 10-fold cross validation
(which makes sure that testing is done on different data from the training information). The records
are the 100 records of the MUC 7 advancement collection. We have included brand-new lessons and
partnerships to the original MUC 7 comments and corrected mistakes and disparities.
Document Processing Rate.
The fastest means of refining records is using the Java API. With this technique Sintelix could refine
1 million XML-encoded wire service reports (2.8 GB of raw papers) per hour on a modern-day 4 core
workstation with 12 GB of RAM. Relying on the network overhead, this speed is about cut in half
when using the web support service interface. If records and notes are kept in Sintelix's data source
just over 600,000 wire service reports are refined each hr.
Search Speed.
We establish Sintelix up on a 4-core 2011 workstation
having actually taken in the 806,000 file Reuters
Corpus. On tests of randomized searches, each
returning the initial 10 instances, the system was
capable of responding to 3000 queries per second.
Equipment Footprint.
Sintelix has been designed to make the best possible usage of the hardware sources. It functions
well on a dual core laptop computer with 4GB of RAM and an SSD disk drive to give an extremely
chic response. In operational applications we suggest that 5GB of RAM be made available to the
program. If refined documents are held within the system's database, we recommend budgeting six
2. times the disk area used for the source records.
Sintelix supplies two-way assimilation. It can be integrated into your workflow via its web services or
through its Java API. Additionally, your content handling and business data sources could be linked
into Sintelix's interior job flow to boost its body removal and resolution capabilities and to put links
from files and notes back to your business information.
Integration into External Work Flows.
The Sintelix API enables access to all its essential abilities via internet services or Java integration.
It's web services are versatile, fast to set up, and normally allow distributed operation. Java
assimilation removes the (sizable) expenses from HTTP and message death over a network. In both
approaches, info is come on the type of XML message, so preventing the complexities of standard
middleware and combination based upon Java items.
Sintelix has a large range of functions to allow you to quickly configure high quality info removal
components for your work moves. It uses novel exclusive language technology, text analytics and
message mining formulas to accomplish high precision at fantastic rate.
Document Intake.
Details Removal Rate.
30 full pages of content per core each 2nd. 2.5 million web pages per core daily.
Sintelix will draw out whatever content it
could locate from files of any type of kind--
consisting of message from executables and
file fragments recovered from hard disks. We
supply the complying with features:.
deNISTing (exemption of computer system
files).
deduplication.
Culling (exclusion) of data by:.
data material type (e.g. binary, application,
picture, etc. - over 1,200 documents types).
data extension (e.g. exe,. inf,. gif, etc.).
language ()FIFTY languages supported).
customer specified data hash list.
to omit unwanted documents.
3. to mark well-known data of interest (e.g. suspect images, infection data or various other files of
passion).
Optionally conserve source files.
Consume stores:.
compression (e.g. zip, bzip, gzip, and so on).
e-mail (PST, MBOX).
Record Normalization.
Paper normalisation handles all the character encoding concerns and extracts document structures
such as paragraphs, tables, headers and so on. This gives the base for succeeding message mining
and evaluation.
Entity Extraction.
Precision.
95 % F1 on MUC 7 papers.
(Called) Body Awareness automatically discovers correct nouns of interest and assign them to
classes, consisting of people, companies and artefacts. Sintelix additionally extracts, days, times,
portions, money quantities and partnerships of different types. Special functions of Sintelix's body
acknowledgment consist of:.
Handles text in:.
combined case (regular).
top case.
reduced instance.
title situation.
Splits of companies into their subcomponents is configurable (e.g. "President James Black" can
additionally be split into a task title and a name).
Can be maximized to your data.
Customers could include their very own hand crafted rules for extraction, combo and removal of
companies using Sintelix's powerful context delicate grammar parser (view below).
Precision.
4. Sintelix Body Recognition has world-leading precision. Sintelix was produced since Australian
Government agencies could possibly not discover entity extraction tools of adequate reliability on
the marketplace.
Accuracy (percent of drawn out entities that Sintelix obtained appropriate - utilizing MUC racking
up algorithm):.
Sintelix 96.21 %; Lead rival (85 % [i.e. Sintelix offers less than a 3rd of the errors]
recall (percentage of real companies that Sintelix discovered - making use of MUC scoring
algorithm):.
Sintelix 94.54 %; Lead rival ( 78 % [i.e. Sintelix offers less than a quarter of the misses out on]
Scalability & Speed. Really quickly-30 full web pages of message per core per second or
2.5 million every day per core( Intel X980 processor chip). Entity Finding.
Clients typically have data sources of entities of passion that they want to identify in their file
collections
. Company Discovering locates recommendation bodies within the documents using the full power of
Sintelix's Company Recognition system. Body Locating occurs
at the very same time as Company Awareness. It makes use of a quickly racked up approximate
matching algorithm, manages pen names and the a number of ways names can be created(e.g. "John
Smith"and "SMITH, John "). Company finding thinks about word frequencies, fame and context,
where offered. Company Resolution & Network Structure( i.e. Identity Resolution, Sense-making ).
Sintelix gives a quite high performance entity resolver that attaches up referrals to the same
underling company across a document collection. It clusters the references, and each collection
describes very same underlying company. As an example, across a paper collection or data set there
may be hundreds references to 3 people called "James Adams". Sintelix Company Resolution creates
a collection of references for every collection. Sintelix's body resolver could be used individually of
the remainder of Sintelix and can be applied to both structured and unstuctured information.
Accuracy. Sintelix has world-leading precision: f-measure is 95.9 % (ideal comparable option on very
same information is
88.2 %). Scalability & Rate. Quite quickly -466,000 companies resolved each min(Intel X980
processor)with similar prices( e.g. R-Swoosh on Oyster)of much less compared to 15,000 each
minute for similar information on similar hardware yet simply doing deterministic body resolution on
structured data.
Such devices fail to use probabilistic contextual restrictions which provide high Entity Recognition
software precision. The services Sintelix offers are:. File Entity Awareness. All optional attributes
such as topic-detection can be accessed by means of this solution. Variations include:. Return a
normalized XML document with entities positioned in-line in text,. Return a normalized XML
document with entities positioned together after the message, and. Storage space of the normalized
document
and extracted bodies within Sintelix's database; return of a paper ID, and optionally, the IDs of the
drawn out entities. The company awareness process is set up and controlled from Sintelix's
Recognize IDE easily accessible from the gps bar. A number of setups can be made available
5. simultaneously. Document handling requests can define the configuration they require.
Common Paper Handling.
The document body awareness support service is just one possible record operations that can be
accessed. Sintelix designers can make entirely brand-new operations customized to your demands.
Data Access from Sintelix's Data source. All the data objects held in Sintelix's database can be
retrieved in serial XML form. Sintelix's search engine result can be gotten as an XML data; and a
record interpretation language is offered to make sure that you can specify the data's framework.
Details Removal. Sintelix's full information removal capacity can be accessed by submitting a record
and the name of the removal template to be made use of. A collection of data source tables
containing the details removed from the paper returned as an SQL file or as an XML file.
Protocols & Efficiency. Several HTTP methods:.
Solitary demand per outlet. Multiple request per outlet.
Limitless connections. Web support service examination collection. Direct Java API. Home windows
or Linux atmospheres. Body removal at operates at about 2 million words per minute on a 4-core
workstation of 2010 vintage.
Without optimization, F1 ratings in the 90-93 % variety
over a basket of company types are most likely.
Complying with some optimization, efficiencies of far better than 95 % are attainable.
Software program Integrations. Semantic Sciences provides integrations with:. ThoughtWeb.
Palantir. Incorporating External
Solutions into Sintelix Work Flows. Sintelix
provides the capability to create plug-ins
that:. allow outside support services to
extend or change process. allow GUI parts to
be developed for setting up exactly how
Sintelix utilizes these exterior support
services.
Web server Equipment Requirements.
Sintelix has been created to make the very
best feasible use of the hardware resources.
It works well on a dual core laptop with 4GB
of RAM and an SSD hard disk drive to supply a really stylish response. In operational applications
6. we suggest that 5GB
of RAM be made available to the program.
If refined documents are held within the device's data source, we advise budgeting six times the disk
area used for the source records. Please call us if you wish to discover about just how Sintelix could
offer more value from your company's files. We could plan demonstations and provide access to
additional documentation. Phone: +61(8)7221 3200.
Fax: +61 (8)7221 3211.
Contact labelmail( at)sintelix.com.