Building next generation data warehousesAlex Meadows
All Things Open 2016 Talk - discussing technologies used to augment traditional data warehousing. Those technologies are:
* data vault
* anchor modeling
* linked data
* NoSQL
* data virtualization
* textual disambiguation
MongoDB and Web Scraping with the Gyes platform. MongoDB Atlanta 2013Jesus Diaz
Gyes is an aggregation platform for the Web. Gyes allows you to develop, schedule and troubleshoot data extraction programs (crawlers) that translate html content into structured data you can use later on. In selecting the data model for the platform, several challenges arose due to the lack of structure of the scraped data, and the need to provide meaningful and efficient access to it. MongoDB was our third rewrite of the Gyes back-end, and by far has exceeded expectations. In this talk, I would like to discuss some of the challenges we faced, and how MongoDB addressed them. Details about implementation challenges are also shared.
IndexedDB - An Efficient Way to Manage Datasara stanford
A web browser standard interface for a local database of records Holds simple values and hierarchical objects. An API for client-side storage of significant amounts of structured data Exhibits High performance. Storage and retrieval done through keys or indexes
General Idea about web mining and different methods of web mining and terminologies associated with web mining and Usage of web mining, differentiation between web mining and data mining.
Building next generation data warehousesAlex Meadows
All Things Open 2016 Talk - discussing technologies used to augment traditional data warehousing. Those technologies are:
* data vault
* anchor modeling
* linked data
* NoSQL
* data virtualization
* textual disambiguation
MongoDB and Web Scraping with the Gyes platform. MongoDB Atlanta 2013Jesus Diaz
Gyes is an aggregation platform for the Web. Gyes allows you to develop, schedule and troubleshoot data extraction programs (crawlers) that translate html content into structured data you can use later on. In selecting the data model for the platform, several challenges arose due to the lack of structure of the scraped data, and the need to provide meaningful and efficient access to it. MongoDB was our third rewrite of the Gyes back-end, and by far has exceeded expectations. In this talk, I would like to discuss some of the challenges we faced, and how MongoDB addressed them. Details about implementation challenges are also shared.
IndexedDB - An Efficient Way to Manage Datasara stanford
A web browser standard interface for a local database of records Holds simple values and hierarchical objects. An API for client-side storage of significant amounts of structured data Exhibits High performance. Storage and retrieval done through keys or indexes
General Idea about web mining and different methods of web mining and terminologies associated with web mining and Usage of web mining, differentiation between web mining and data mining.
In this talk we will discuss uses of BSON outside of MongoDB -- for example for IPC in web services infrastructure and mobile applications. Some standalone BSON command line utilities will be demoed.
it gives you short and brief information about web mining and its different types.it will be helpful to understand web mining and its appliction well. it contains particular topic well with some slides
Semantic technologies offer a wide range of benefits in an increasing number of application fields such as data management, business intelligence, machine learning etc.
from Christian Opitz | Head of innovation at Netresearch GmbH & Co. KG
Presentation at Semantics 2016 in Leipzig in the context with the results of the LEDS project
A large part of the free knowledge existing on the Web is available as heterogeneous, semi-structured data, which is only weakly interlinked and in general does not include any semantic classification.
Michael Krug | Technische Universität Chemnitz
Presentation at Semantics 2016 in Leipzig in the context with the results of the LEDS project
Large corporations have to master vast amounts of heterogeneous data in order to stay competitive. While existing approaches have attempted to consolidate and manage the data by forcing it into a single shared data model, data lakes recently emerged that instead provide a central storage point for holding all data sets in their original form.
In this talk, we present eccenca CorporateMemory, which extends the data lake paradigm with a semantic integration layer for managing diverse, but semantically enriched data. eccenca CorporateMemory builds an extensible knowledge graph that employs RDF vocabularies for transforming and linking multiple datasets in order to generate an integrated semantic understanding of the data.
Robert Isele | Head of Data Integration Unit at eccenca GmbH
Presentation at Semantics 2016 in Leipzig in the context with the results of the LEDS project
As technology and needs evolve and the need for scalable and high availability solutions increase there is a need to evaluate new databases. The lack of clarity in the market makes in difficult for IT stakeholders to understand the differences between the solutions available and the choice to make. The key areas to consider while evaluating NoSql databases are data model, query model, consistency model, APIs, support and community strength.
Enterprise Search Summit Keynote: A Big Data Architecture for SearchSearch Technologies
This presentation was given by Search Technologies' CEO Kamran Khan at the November 2013 Enterprise Search Summit / KMWorld in Washington DC. He discussed how modern search engines are currently being combined with powerful independent content processing pipelines and the distributed processing technologies from big data to form new and exciting enterprise search architecture, delivering results only available to the biggest companies with the deepest pockets in the past. For more information visit http://www.searchtechnologies.com/.
Citing a number of uses cases, Kamran Khan, CEO of Search Technologies, presented a keynote address at the KMWorld 2016 conference in Washington, DC about the evolution of search and big data.
This presentation, hold during Semantcs conference, introduce Ontos' current achievement towards a Streaming-based Text Mining solution by using Deep Learning and Semantic Web technologies.
International Journal of Database Management Systems (IJDBMS)ijfcst journal
International Journal of Database Management Systems (IJDBMS) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the database management systems & its applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern developments in this field, and establishing new collaborations in these areas.Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Database management systems.
Solution architecture for big data projects
solution architecture,big data,hadoop,hive,hbase,impala,spark,apache,cassandra,SAP HANA,Cognos big insights
On-Demand RDF Graph Databases in the CloudMarin Dimitrov
slides from the S4 webinar "On-Demand RDF Graph Databases in the Cloud"
RDF database-as-a-service running on the Self-Service Semantic Suite (S4) platform: http://s4.ontotext.com
video recording of the talk is available at http://info.ontotext.com/on-demand-rdf-graph-database
In this talk we will discuss uses of BSON outside of MongoDB -- for example for IPC in web services infrastructure and mobile applications. Some standalone BSON command line utilities will be demoed.
it gives you short and brief information about web mining and its different types.it will be helpful to understand web mining and its appliction well. it contains particular topic well with some slides
Semantic technologies offer a wide range of benefits in an increasing number of application fields such as data management, business intelligence, machine learning etc.
from Christian Opitz | Head of innovation at Netresearch GmbH & Co. KG
Presentation at Semantics 2016 in Leipzig in the context with the results of the LEDS project
A large part of the free knowledge existing on the Web is available as heterogeneous, semi-structured data, which is only weakly interlinked and in general does not include any semantic classification.
Michael Krug | Technische Universität Chemnitz
Presentation at Semantics 2016 in Leipzig in the context with the results of the LEDS project
Large corporations have to master vast amounts of heterogeneous data in order to stay competitive. While existing approaches have attempted to consolidate and manage the data by forcing it into a single shared data model, data lakes recently emerged that instead provide a central storage point for holding all data sets in their original form.
In this talk, we present eccenca CorporateMemory, which extends the data lake paradigm with a semantic integration layer for managing diverse, but semantically enriched data. eccenca CorporateMemory builds an extensible knowledge graph that employs RDF vocabularies for transforming and linking multiple datasets in order to generate an integrated semantic understanding of the data.
Robert Isele | Head of Data Integration Unit at eccenca GmbH
Presentation at Semantics 2016 in Leipzig in the context with the results of the LEDS project
As technology and needs evolve and the need for scalable and high availability solutions increase there is a need to evaluate new databases. The lack of clarity in the market makes in difficult for IT stakeholders to understand the differences between the solutions available and the choice to make. The key areas to consider while evaluating NoSql databases are data model, query model, consistency model, APIs, support and community strength.
Enterprise Search Summit Keynote: A Big Data Architecture for SearchSearch Technologies
This presentation was given by Search Technologies' CEO Kamran Khan at the November 2013 Enterprise Search Summit / KMWorld in Washington DC. He discussed how modern search engines are currently being combined with powerful independent content processing pipelines and the distributed processing technologies from big data to form new and exciting enterprise search architecture, delivering results only available to the biggest companies with the deepest pockets in the past. For more information visit http://www.searchtechnologies.com/.
Citing a number of uses cases, Kamran Khan, CEO of Search Technologies, presented a keynote address at the KMWorld 2016 conference in Washington, DC about the evolution of search and big data.
This presentation, hold during Semantcs conference, introduce Ontos' current achievement towards a Streaming-based Text Mining solution by using Deep Learning and Semantic Web technologies.
International Journal of Database Management Systems (IJDBMS)ijfcst journal
International Journal of Database Management Systems (IJDBMS) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the database management systems & its applications. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding Modern developments in this field, and establishing new collaborations in these areas.Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Database management systems.
Solution architecture for big data projects
solution architecture,big data,hadoop,hive,hbase,impala,spark,apache,cassandra,SAP HANA,Cognos big insights
On-Demand RDF Graph Databases in the CloudMarin Dimitrov
slides from the S4 webinar "On-Demand RDF Graph Databases in the Cloud"
RDF database-as-a-service running on the Self-Service Semantic Suite (S4) platform: http://s4.ontotext.com
video recording of the talk is available at http://info.ontotext.com/on-demand-rdf-graph-database
“not only SQL.”
NoSQL databases are databases store data in a format other than relational tables.
NoSQL databases or non-relational databases don’t store relationship data well.
This ppt explain about choosing your NoSQL database. This also contains factors which needs to be consider while choosing NoSQL database. Thanks Arun Chandrasekaran(https://www.linkedin.com/profile/view?id=AAMAAAQKxWsB9tkk7s2ll2T2BvLvR9QDv_OdJXs&trk=hp-identity-name) for helping me.
A column oriented database, rather a columnar database is a DBMS (Database Management System) that stores data in columns instead of rows. A columnar database aims to efficiently write and read data to and from hard disk storage to speed up the time to execute a query. A column-store is a physical concept. Here, I primarily focus on what a columnar database is, how it works, its advantages, disadvantages and applications at current times. In due course, the top three market selling columnar databases are discussed with their features. Thus, it is seen that, columnar database is an emerging concept which has high prospect in coming future.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
2. What Is NoSQL ?
• Managing very large sets of distributed data.
• Analyze massive amounts of unstructured data.
• Originally created and used by Internet leaders such
as Facebook, Google, Amazon and others.
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4. Benefits of NoSQL
• Schema agnostic
• Scalability
• Performance
• High availability
• Global availability
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5. Architecture Patterns
• Allow you to give precise names to recurring high
level data storage patterns.
• A consistent process that allows you to name the
pattern
• All team members should have the same
understanding about how a particular pattern
solves your problem
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6. • Key-Value Store
• Document Store
• Column Store
• Graph Base
Types : Architecture Pattern
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7. Key-value store
• The simplest NoSQL data stores to use from an
API perspective.
• Client can get the value for the key, put a value
for a key or delete a key.
• Have great performance and can be easily scaled.
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9. Key-value store - API
• Get – to get the value associated with the key.
• Put – to associate the value with the key.
• Multi-get – to get the list of values associated
with the keys.
• Delete – to remove the data for the key.
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10. Key-value store - Rules
• Distinct Keys :- All keys in key value store are
unique.
• No queries on values :- No queries can be
performed on the values of the table.
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11. Key-value store - Weakness
• Lack of Consistency
• Volume of the data increases since difficult to
maintain unique value as keys.
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12. Key-value store - Uses
Word (key)
• Dictionary :
Meanings (value)
Website (key)
• Google :
www.tutorialpoints.com (value)
www.gmail.com (value)
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14. Document store
• Documents are the main concept in document
databases.
• More semi-structured data.
• The database stores and retrieves documents,
which can be XML, JSON, BSON, and so on.
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16. Document store : Example
• MongoDB Stores data in Documents.
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17. Column store
• Store data in column families as rows that have many
columns associated with a row key.
• Column families are groups of related data that is often
accessed together.
• When a column consists of a map of columns, then we have
a super column.
• A super column consists of a name and a value which is a
map of columns.
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19. Column Store : Benefits
• Column stores are very efficient at data
compression
• Columnar databases are very scalable.
• Columnar stores can be loaded extremely fast.
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20. Column Store : Example
• Hbase – Hadoop Database is a NoSQL database
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21. Graph Base
• Stores entities and relationships between these
entities.
• Relations are known as edges.
• Nodes are organized by relationships which allow you
to find interesting patterns between the nodes.
• Interpreted data in different ways based on
relationships.
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23. Graph Base : Example
• Neo4J - graph database
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24. Comparison
Parameters
Key - Value
Store
Document
Store
Column Store Graph Base
Performance High High High Variable
Scalability High Variable High Variable
Flexibility High High Moderate High
Complexity None Low Low High
Functionality Variable Variable Minimal Graph theory
Example
Purchasing
Product Bill
(Amazon)
Company
Store User
details
(Facebook)
Movies
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