If you think you’ve got a data warehouse, or don’t even need one, you might want to think again. Our CEO John Peterson gives real-life, practical reasons for why you still need a real data warehouse…and tells the advantages of properly staged data.
Polestar we hope to bring the power of data to organizations across industries helping them analyze billions of data points and data sets to provide real-time insights, and enabling them to make critical decisions to grow their business.
SALES BASED DATA EXTRACTION FOR BUSINESS INTELLIGENCEcscpconf
Data warehouse use has increased significantly in recent years and now plays a fundamental role in many organizations’ decision-support processes. An effective business intelligence
infrastructure that leverages the power of a data warehouse can deliver value by helping companies enhance their customer experience. In this paper is to generate reports with various
drilldowns and slier conditions with suitable parameters which provide a complete business solution which is helpful for monitor the company inflow and outflow. The goal of the work is
for potential users of the data warehouse in their decision making process in the Business process system to get a complete visual effort of those reports by creating the chart and grid interface from warehouse. The example in this paper relate directly to the Adventure Work Data Warehouse Project implementation which helps to know the internet sales amount according to different date
HYBRID DATABASE SYSTEM FOR BIG DATA STORAGE AND MANAGEMENTIJCSEA Journal
Relational database systems have been the standard storage system over the last forty years. Recently, advancements in technologies have led to an exponential increase in data volume, velocity and variety beyond what relational databases can handle. Developers are turning to NoSQL which is a non- relational database for data storage and management. Some core features of database system such as ACID have been compromised in NOSQL databases. This work proposed a hybrid database system for the storage and management of extremely voluminous data of diverse components known as big data, such that the two models are integrated in one system to eliminate the limitations of the individual systems. The system is
implemented in MongoDB which is a NoSQL database and SQL. The results obtained, revealed that having these two databases in one system can enhance storage and management of big data bridging the gap between relational and NoSQL storage approach.
Polestar we hope to bring the power of data to organizations across industries helping them analyze billions of data points and data sets to provide real-time insights, and enabling them to make critical decisions to grow their business.
SALES BASED DATA EXTRACTION FOR BUSINESS INTELLIGENCEcscpconf
Data warehouse use has increased significantly in recent years and now plays a fundamental role in many organizations’ decision-support processes. An effective business intelligence
infrastructure that leverages the power of a data warehouse can deliver value by helping companies enhance their customer experience. In this paper is to generate reports with various
drilldowns and slier conditions with suitable parameters which provide a complete business solution which is helpful for monitor the company inflow and outflow. The goal of the work is
for potential users of the data warehouse in their decision making process in the Business process system to get a complete visual effort of those reports by creating the chart and grid interface from warehouse. The example in this paper relate directly to the Adventure Work Data Warehouse Project implementation which helps to know the internet sales amount according to different date
HYBRID DATABASE SYSTEM FOR BIG DATA STORAGE AND MANAGEMENTIJCSEA Journal
Relational database systems have been the standard storage system over the last forty years. Recently, advancements in technologies have led to an exponential increase in data volume, velocity and variety beyond what relational databases can handle. Developers are turning to NoSQL which is a non- relational database for data storage and management. Some core features of database system such as ACID have been compromised in NOSQL databases. This work proposed a hybrid database system for the storage and management of extremely voluminous data of diverse components known as big data, such that the two models are integrated in one system to eliminate the limitations of the individual systems. The system is
implemented in MongoDB which is a NoSQL database and SQL. The results obtained, revealed that having these two databases in one system can enhance storage and management of big data bridging the gap between relational and NoSQL storage approach.
Proceedings of the 2nd International REA Technology Workshop - 2006Demetrius_Gallitzin
Piet Erik Adolf Vandenbossche and J.C. Wortmann:
"Why accounting data models from research are not incorporated in ERP systems",
Mette Jaquet:
"A Property Driven Approach towards Describing Semantics of REA Entities",
Frederik Gailly and Geert Poels:
"Towards an Operational REA Ontology Using Web Ontology Languages",
Fred van Blommestein:
"REA as an e-business ontology",
Anders Hessellund:
"Modeling Issues in REA",
Pavel Hruby and Jesper Kiehn:
"Revised Classification of the Enterprise Information Architecture Elements"
Databases are growing at amazing rates. You need to properly handle place and pay particular attention to the place used by information and database records. Also, with Fst Restoration Area (FRA) there are back-up places that need to be handled for their place utilization.
Graph Based Workload Driven Partitioning System by Using MongoDBIJAAS Team
The web applications and websites of the enterprises are accessed by a huge number of users with the expectation of reliability and high availability. Social networking sites are generating the data exponentially large amount of data. It is a challenging task to store data efficiently. SQL and NoSQL are mostly used to store data. As RDBMS cannot handle the unstructured data and huge volume of data, so NoSQL is better choice for web applications. Graph database is one of the efficient ways to store data in NoSQL. Graph database allows us to store data in the form of relation. In Graph representation each tuple is represented by node and the relationship is represented by edge. But, to handle the exponentially growth of data into a single server might decrease the performance and increases the response time. Data partitioning is a good choice to maintain a moderate performance even the workload increases. There are many data partitioning techniques like Range, Hash and Round robin but they are not efficient for the small transactions that access a less number of tuples. NoSQL data stores provide scalability and availability by using various partitioning methods. To access the Scalability, Graph partitioning is an efficient way that can be easily represent and process that data. To balance the load data are partitioned horizontally and allocate data across the geographical available data stores. If the partitions are not formed properly result becomes expensive distributed transactions in terms of response time. So the partitioning of the tuple should be based on relation. In proposed system, Schism technique is used for partitioning the Graph. Schism is a workload aware graph partitioning technique. After partitioning the related tuples should come into a single partition. The individual node from the graph is mapped to the unique partition. The overall aim of Graph partitioning is to maintain nodes onto different distributed partition so that related data come onto the same cluster.
● Data Modeling and Data Models.
● Business Rules (Translating Business Rules into Data Model Components).
● Emerging Data Models: Big Data and NoSQL.
● Degrees of Data Abstraction (External, Conceptual, Internal and Physical model).
Data Models [DATABASE SYSTEMS: Design, Implementation, and Management]Usman Tariq
In this PPT, you will learn:
• About data modeling and why data models are important
• About the basic data-modeling building blocks
• What business rules are and how they influence database design
• How the major data models evolved
• About emerging alternative data models and the needs they fulfill
• How data models can be classified by their level of abstraction
Author: Carlos Coronel | Steven Morris
https://www.golodiuk.com/service/about-enterprise-integration
Introduction to Enterprise Integration as one of the very crucial and sometimes even a necessary part of many IT solutions for business.
A Review of Data Access Optimization Techniques in a Distributed Database Man...Editor IJCATR
In today's computing world, accessing and managing data has become one of the most significant elements. Applications as
varied as weather satellite feedback to military operation details employ huge databases that store graphics images, texts and other
forms of data. The main concern in maintaining this information is to access them in an efficient manner. Database optimization
techniques have been derived to address this issue that may otherwise limit the performance of a database to an extent of vulnerability.
We therefore discuss the aspects of performance optimization related to data access in distributed databases. We further looked at the
effect of these optimization techniques
A Review of Data Access Optimization Techniques in a Distributed Database Man...Editor IJCATR
In today's computing world, accessing and managing data has become one of the most significant elements. Applications as
varied as weather satellite feedback to military operation details employ huge databases that store graphics images, texts and other
forms of data. The main concern in maintaining this information is to access them in an efficient manner. Database optimization
techniques have been derived to address this issue that may otherwise limit the performance of a database to an extent of vulnerability.
We therefore discuss the aspects of performance optimization related to data access in distributed databases. We further looked at the
effect of these optimization techniques.
Proceedings of the 2nd International REA Technology Workshop - 2006Demetrius_Gallitzin
Piet Erik Adolf Vandenbossche and J.C. Wortmann:
"Why accounting data models from research are not incorporated in ERP systems",
Mette Jaquet:
"A Property Driven Approach towards Describing Semantics of REA Entities",
Frederik Gailly and Geert Poels:
"Towards an Operational REA Ontology Using Web Ontology Languages",
Fred van Blommestein:
"REA as an e-business ontology",
Anders Hessellund:
"Modeling Issues in REA",
Pavel Hruby and Jesper Kiehn:
"Revised Classification of the Enterprise Information Architecture Elements"
Databases are growing at amazing rates. You need to properly handle place and pay particular attention to the place used by information and database records. Also, with Fst Restoration Area (FRA) there are back-up places that need to be handled for their place utilization.
Graph Based Workload Driven Partitioning System by Using MongoDBIJAAS Team
The web applications and websites of the enterprises are accessed by a huge number of users with the expectation of reliability and high availability. Social networking sites are generating the data exponentially large amount of data. It is a challenging task to store data efficiently. SQL and NoSQL are mostly used to store data. As RDBMS cannot handle the unstructured data and huge volume of data, so NoSQL is better choice for web applications. Graph database is one of the efficient ways to store data in NoSQL. Graph database allows us to store data in the form of relation. In Graph representation each tuple is represented by node and the relationship is represented by edge. But, to handle the exponentially growth of data into a single server might decrease the performance and increases the response time. Data partitioning is a good choice to maintain a moderate performance even the workload increases. There are many data partitioning techniques like Range, Hash and Round robin but they are not efficient for the small transactions that access a less number of tuples. NoSQL data stores provide scalability and availability by using various partitioning methods. To access the Scalability, Graph partitioning is an efficient way that can be easily represent and process that data. To balance the load data are partitioned horizontally and allocate data across the geographical available data stores. If the partitions are not formed properly result becomes expensive distributed transactions in terms of response time. So the partitioning of the tuple should be based on relation. In proposed system, Schism technique is used for partitioning the Graph. Schism is a workload aware graph partitioning technique. After partitioning the related tuples should come into a single partition. The individual node from the graph is mapped to the unique partition. The overall aim of Graph partitioning is to maintain nodes onto different distributed partition so that related data come onto the same cluster.
● Data Modeling and Data Models.
● Business Rules (Translating Business Rules into Data Model Components).
● Emerging Data Models: Big Data and NoSQL.
● Degrees of Data Abstraction (External, Conceptual, Internal and Physical model).
Data Models [DATABASE SYSTEMS: Design, Implementation, and Management]Usman Tariq
In this PPT, you will learn:
• About data modeling and why data models are important
• About the basic data-modeling building blocks
• What business rules are and how they influence database design
• How the major data models evolved
• About emerging alternative data models and the needs they fulfill
• How data models can be classified by their level of abstraction
Author: Carlos Coronel | Steven Morris
https://www.golodiuk.com/service/about-enterprise-integration
Introduction to Enterprise Integration as one of the very crucial and sometimes even a necessary part of many IT solutions for business.
A Review of Data Access Optimization Techniques in a Distributed Database Man...Editor IJCATR
In today's computing world, accessing and managing data has become one of the most significant elements. Applications as
varied as weather satellite feedback to military operation details employ huge databases that store graphics images, texts and other
forms of data. The main concern in maintaining this information is to access them in an efficient manner. Database optimization
techniques have been derived to address this issue that may otherwise limit the performance of a database to an extent of vulnerability.
We therefore discuss the aspects of performance optimization related to data access in distributed databases. We further looked at the
effect of these optimization techniques
A Review of Data Access Optimization Techniques in a Distributed Database Man...Editor IJCATR
In today's computing world, accessing and managing data has become one of the most significant elements. Applications as
varied as weather satellite feedback to military operation details employ huge databases that store graphics images, texts and other
forms of data. The main concern in maintaining this information is to access them in an efficient manner. Database optimization
techniques have been derived to address this issue that may otherwise limit the performance of a database to an extent of vulnerability.
We therefore discuss the aspects of performance optimization related to data access in distributed databases. We further looked at the
effect of these optimization techniques.
MIT Case Study: Learning how to work smarter at PSP InvestmentsFrançois Bélanger
Jeanne Ross, Director at MIT Sloan School of Management and author of "Enterprise Architecture as Strategy" wrote this case study about my work at PSP.
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Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
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Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
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Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
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Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
30 Reasons You Still Need to Stage Your Data Senturus Blog
1. 30 Reasons You Still Need to Stage Your Data
www.senturus.com/blog/30-reasons-you-still-need-to-stage-your-data/
November 29, 2016 Business Strategy & Perspectives
If you sit in on Senturus team meetings, you’ll hear impassioned discussions about how folks need to properly stage
their data. And we’re not talking about merely replicating tables in a separate database offline and calling that a data
warehouse. We mean a high performance, business-centric, subject-area-organized system, aligned with the
company’s strategic goals. One with validated and secure metrics that is easy to pull data from and as a result
enjoys high adoption rates across the organization.
As technologies advance and data types grow, the more we see the need for solidly architected data warehouses.
Our CEO and co-founder John Peterson can wax poetic on the subject. He recently pulled together this quick list of
real-life, practical reasons why a data warehouse plays such an important role in any data driven organization.
(We’d bet John’s got another good 30 reasons in his back pocket!)
To make data truly usable and valuable, it needs to be transformed into business centric terms and metrics that
support organizational decision-making and enriched to provide roll up and drill down access into information. This
transformation needs to take place somewhere between the raw source and the final report/analysis. It becomes a
question of where and how you handle that transformation, upstream or downstream.
Addressing the transformation downstream simply forces business end-users to tackle it themselves, which leads to
the horribly time consuming process of what we call “Excel hell.” It also results in complex, slow and inaccurate self-
service dashboards and reports and data “acquisition aggravation.”
Senturus recommends you do the work upstream by properly staging your data to avoid “Excel hell”… and for at
least 30 other good reasons.
30 Reasons You Still Need to Stage Data
1. Consolidates multiple sources of data
2. Retains history when changing/upgrading systems
3. Captures snapshots and realigns data
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2. 4. Consolidates data from cloud and on-premise
5. Provides persistent storage of critical data
6. Increases efficiency by storing data only once
7. Cleanses data
8. Handles and fixes NULL values
9. Applies universal, one-time filters
10. Improves performance (end-user reports, etc.)
11. Eliminates “expensive” and incorrect joins
12. Transforms complex source data into usable facts
13. Captures strategic business-centric metrics
14. Consolidates and simplifies disparate attribute data
15. Adds mandatory business “dimensional richness”
16. Simplifies complex data relationships
17. Applies logic to complete and align data
18. Facilitates allocation and attribution
19. Provides “insulating layer” from source systems
20. Eliminates complex logic needed in BA layer(s)
21. Allows for “slow-changing dimensions”
22. Captures “slow-changing facts”
23. Eliminates live connections to source data
24. Eliminates spreadmarts and local databases
25. Reduces challenges associated when key person(s) leave
26. Eliminates incorrect calculations by end-users
27. Provides security and controlled access
28. Helps reduce software license fees
29. Enables consolidated dashboards and aligned metrics
30. Enables better dashboards thru context
Benefits of Properly Staged Data
Properly staged data offer numerous strategic advantages to an organization. Benefits include:
Better decisions
Faster actions
Unified strategic direction: what gets measured, gets managed
Greater efficiency: less time in "Excel hell"
Less redundancy and waste
Fewer errors: some can cost $millions
Happier business users
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3. Greater user adoption
For more on this topic, you can hear direct from John in his recorded webinar Death of the Data Warehouse?
Stay on top of business intelligence topics, read other Senturus blogs at: http://www.senturus.com/blog/.
Data Prep
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