Calligraph is a business intelligence tool that allows for flexible and on-demand multidimensional analysis of data without pre-processing. It delivers integrated OLAP and reporting functionality to support decision making. Unlike traditional BI that requires pre-building cubes, Calligraph translates tables into linear query sets and allows users to directly explore data through a customizable semantic layer in their own terms. This provides real-time decision support without consultants or delays.
This is the 3- Tier architecture of Data Warehouse. This is the topic under Data Mining subject. Data mining is extracting knowledge from large amount of data.
Catalogic DPX: Dashboard Reporting with Microsoft Power BICatalogic Software
Dashboard Reporting for Catalogic DPX is an easy to use, fully customizable reporting tool that lets you quickly see the overall status of your DPX environment, while also offering drill-down into details. Microsoft Power BI provides the end-user interface to DPX data collected by a custom Catalogic utility. The result is a detailed and insightful view of your DPX enterprise that provides at-a-glance information about job status, job performance, media utilization, helps identify problem areas and more.
This is the 3- Tier architecture of Data Warehouse. This is the topic under Data Mining subject. Data mining is extracting knowledge from large amount of data.
Catalogic DPX: Dashboard Reporting with Microsoft Power BICatalogic Software
Dashboard Reporting for Catalogic DPX is an easy to use, fully customizable reporting tool that lets you quickly see the overall status of your DPX environment, while also offering drill-down into details. Microsoft Power BI provides the end-user interface to DPX data collected by a custom Catalogic utility. The result is a detailed and insightful view of your DPX enterprise that provides at-a-glance information about job status, job performance, media utilization, helps identify problem areas and more.
Migrating from CA AllFusionTM ERwin® Data Modeler to Embarcadero ER/StudioMichael Findling
This is a step-by-step guide to migrating from CA AllFusionTM ERwin Data Modeler to Embarcadero ER/Studio - the next-generation data modeling solutions. Embarcadero Technologies is the leading provider of database tools and developer software.
The objective of this investigation is to predict the behavior of the decision of a customer on a car model based on given six features. Features being Buying Price, Maintenance price, Number of doors, Seaters, Luggage space, and Safety.
User can run queries via MicroStrategy’s visual interface without the need to write unfamiliar HiveQL or MapReduce scripts. In essence, any user, without programming skill in Hadoop, can ask questions against vast volumes of structured and unstructured data to gain valuable business insights.
Migrating from CA AllFusionTM ERwin® Data Modeler to Embarcadero ER/StudioMichael Findling
This is a step-by-step guide to migrating from CA AllFusionTM ERwin Data Modeler to Embarcadero ER/Studio - the next-generation data modeling solutions. Embarcadero Technologies is the leading provider of database tools and developer software.
The objective of this investigation is to predict the behavior of the decision of a customer on a car model based on given six features. Features being Buying Price, Maintenance price, Number of doors, Seaters, Luggage space, and Safety.
User can run queries via MicroStrategy’s visual interface without the need to write unfamiliar HiveQL or MapReduce scripts. In essence, any user, without programming skill in Hadoop, can ask questions against vast volumes of structured and unstructured data to gain valuable business insights.
]project-open[ Reporting & Indicators Options: This tutorial explains a number of different ways to extract information from ]project-open[ as a kind of “report”
This PPT will help for SAP Interview Questions particularly SAP domain Candidates. for more information please login to www.rekruitin.com
By ReKruiTIn.com
Hovitaga OpenSQL Editor is a powerful tool for SAP consultants, ABAP developers and basis administrators that helps to work with the database of an SAP system.
This paper gives an overview of the product.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
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).
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
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
2. Common Issues of Traditional BI
High reliance on pre-processing of information (cubes
and views) limits user ability to explore the data beyond
pre-programmed reports
Explosion of data stored in the DB – less than 10% of
productive data, rest is derived data
Difficult for users to create new reports, which basically
prevents managers and other decision makers from
using the tool in day to day work for real time decision
making support
Expensive consultants are needed to re-program reports
– costing both money and time
3. Short overview of Calligraph terminology and
design principles
Calligraph delivers integrated OLAP + Query & Reporting
functionality for decision making support - On Line, On The Fly,
On Demand;
Calligraph is aimed at the end user without any programming
skills
User interacts with Calligraph in his native language and in his
subject matter terms. We call it User Semantic Layer. User
Semantic Layer is created according to user rights to access the
data
Tables of any complexity and size and in any theoretical
representation are translated into linear set of queries with
automatic parallelization. This is a key characteristic which
prevents “information blast” and allows to keep information
processing time linearly dependent on the volume of information
being processed.
4. Calligraph technology benefits
Flexible multidimensional on-line analysis of recent data
for decision making support
User generates queries and reports via direct interaction
with the system in terms of his subject domain
Semantic layer of the user is formed in a strict conformity
with his rights to access the data
Queries are highly parallelizable and their structure
allows optimal execution
Supports connection to any relational DB via OLEDB
Full conformity to all 12 classical OLAP rules
5. Types of tables formed by Calligraph
Listing table example Analytic (or cross) table
example
6. Important notes to the previous slide
There are only two principal types of homogeneous tables:
Listing tables
Analytical tables (cross tables)
Any other table is non-homogeneous and can be
decomposed into components of either listing or analytic
tables
Ergonomics asserts that human perception can only get
homogeneous tables easily, any non-homogeneous
(composed) table will be perceived partially, by picking
out and analyzing homogeneous components
7. Definition of “Task”
User (such as manager) can have access to different
types of information – for example, commercial, HR,
logistics and warehouse, finance, etc – from different
DBs deployed by the enterprise
To ease perception, User semantic layer can be logically
split into linked fragments, which we call Task:
“Commerce”, “HR”, “Warehouse”, “Finance” etc.
Technically, Task is a set of fields from different DB
tables with all necessary connections between them.
Each field has its own user-friendly alias. Thus we create
an environment which is clear and convenient to the end
user.
9. Configuration of the user semantic layer (field names
and mapping)
De facto, this is example of manual creation of User Semantic
Layer (automatic creation is also possible)
10. Semantic User Layer
Is a list of field names accessible to the user, in user language
and in user subject domain terms
11. Definition of “Gradation”
Gradation is any field from the user semantic layer with
a set of boundary conditions
Boundary conditions for the gradation are connected by
logical “OR”
Conditions can be grouped into simple or extended totals
Gradation is used to create a dimension in analytic table,
in the filter or in “master-detail” section
12. Difference from OLAP using cubes
Any field from the user semantic layer can form a dimension
for analytical table
All DB fields are “equal”, without separating them into
“dimensions” and “facts”
Boundary conditions for the gradations can also be
described as range, mask or a formula
User can create “virtual” gradation (i.e. the gradation which
is calculated by applying a formula), enabling “what-if”
analysis on the fly
No need to perform pre-processing and create (and then
continually increment) cubes, which limits user ability to
perform analysis in a way he/she needs, as user can specify
any dimension through direct interaction with Calligraph
User creates table template in any theoretically possible
view on-line
All queries are performed on-line and can be parallelized
13. Definition of “Filter”
We use filter if we need apply certain conditions to all data
in the particular query
Gradation is the minimal element to form the filter
Several gradations connected by a logical “AND” are
called aggregate
Filter is a set of aggregates which are connected by
logical “AND” or “OR” (in any order)
Calligraph sets no limits on the “depth” of the filter and its
length
Filters give user a very easy and visual way to create data
filtering rules on the fly
14. Definition of ”Master-Detail”
Any complex table can be automatically split into a set of
simpler tables by drag and drop of any gradation in
“master-detail” query
Simpler table are formed by using dropped gradation
boundary conditions to select the information
Example: analytic report on EMC business around the
world can contain gradation “Continents”. If user moves
this gradation in “master-detail”, then complex table will
be split into several simpler tables which contain only
information about business in every continent. If you
further move gradation “country”, then every table
containing information on continents will be further split
in several tables with information on every country.
15. Definition of “Drill-Down”
Any cell of the analytical table contains data which was
filtered based on the boundary conditions set for its column
and row, as well as those defined by the “master-detail”.
Decision making often requires detailed understanding of
the information in the analytical table – such as to
understand the reasons behind unsatisfactory results.
Calligraph provides an easy way to achieve this, with
maximum allowed detailing according to user rights for
data access.
User can select a cell (or cells) of the table and press
“Drill-Down”, and get automatically generated listing table
with all fields from the analytical table.
16. Demo block diagram
Greenplum Master Server
Segment Segment SegmentSegment
Windows Client Machine
Calligraph
Remote Desktop
Sample Database
18. Current status of Calligraph
Version 5.2 is available as a standalone Windows
application
Hundreds of copies have been sold and are being used
within big and small enterprises
Some of the Calligraph enterprise customers include
Atommash, Novorosiyskiy port, in big medical institutes and
hospitals, in government (Republican Statistical Service,
Russian State Parliament, Tax authorities, police
departments, etc) and in small and medium businesses.
Calligraph is registered in the Russian agency of patents
and trademarks
19. Possible ways to further develop
Calligraph technology
Cloud service
External reporting unit for CASE system
Automatic configuration
Support for Hadoop
Voice input etc.
20. Benefits of Calligraph to
EMC/Greenplum
Full alignment with Greenplum data analytics and
exploration focus
On demand, on the fly analysis
Parallelization and speed of query execution
Calligraph can be developed as cloud service, giving
access to data analytics to every user in the enterprise
Loyalty of users through ease of use and convenience
Highly competitive offer in terms of functionality and
price
Easier to demonstrate business value of Greenplum DB
and data analytics to the customers
Editor's Notes
Есть только два принципиально разных типов однородных таблиц: списковые и аналитические.
Настройка сделана через ODBC.
Для подключения к БД GreenPlum использован рекомендованный специалистами EMC драйвер к СУБД PostgreSQL.