This document defines and describes a decision support system (DSS). A DSS is a computer-based information system that supports business or organizational decision-making. There are several types of DSS, including model-driven, data-driven, knowledge-driven, document-driven, communication-driven, and web-based systems. A DSS has characteristics like facilitating decision-making, supporting interaction, repeated use, and task orientation. The objectives of a DSS are to increase effectiveness of decision-making processes while not replacing human decision makers. A DSS has components like inputs, user knowledge, outputs, and decisions.
MODEL- DRIVEN DSS
includes system that use accounting, financial models, and representational models.
2. DATA DRIVEN DSS
file drawer & management reporting system, data warehousing, geographical information.
MODEL- DRIVEN DSS
includes system that use accounting, financial models, and representational models.
2. DATA DRIVEN DSS
file drawer & management reporting system, data warehousing, geographical information.
Decision Support System - Management Information SystemNijaz N
Refers to class of system which supports in the process of decision making and does not always give a decision itself.
Decision Support Systems supply computerized support for the decision making process.
Decision Support Systems: Concept, Constructing a DSS, Executive Information ...Ashish Hande
Decision Support Systems: Concept, Constructing a DSS,
Executive Information System, (EIS), Artifical Intelligence
System (AIS), knowledge Based Expert System (KBES),
Enterprise Management System (EMS), Decision Support
Management System (DSMS).
Decision Support System - Management Information SystemNijaz N
Refers to class of system which supports in the process of decision making and does not always give a decision itself.
Decision Support Systems supply computerized support for the decision making process.
Decision Support Systems: Concept, Constructing a DSS, Executive Information ...Ashish Hande
Decision Support Systems: Concept, Constructing a DSS,
Executive Information System, (EIS), Artifical Intelligence
System (AIS), knowledge Based Expert System (KBES),
Enterprise Management System (EMS), Decision Support
Management System (DSMS).
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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.
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.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
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
2. What is DSS?
⚫A DSS is a computer-based information system that
supports businessororganizational decision-making
activities.
⚫A DSS is a collection of integrated software
applications and hardware that form the backbone of
an organization’s decision making processand help to
make decisions, which may be rapidly changing and
noteasily specified in advance.
3. TYPES OF DSS
1. MODEL- DRIVEN DSS
includes system that use accounting, financial models, and
representational models.
2. DATA DRIVEN DSS
file drawer & management reporting system, data
warehousing, geographical information.
4. TYPES OF DSS
3. KNOWLEDGE DRIVEN DSS
are computer systems with specialized problem solving expertise.
4. DOCUMENT DRIVEN DSS
is evolving to help manager retrieves & manage unstructured document.
5. TYPES OF DSS
5. COMMUNICATION DRIVEN GROUP
DSS
includes communication, collaboration and
DSS technologies that do not fit within those
DSS type hybrid.
6. INTRA ORGANIZATIONAL DRIVEN
DSS
are design for use by individuals to a
company as standalone DSS or use group of
6. TYPES OF DSS
7. INTER ORGANIZATION DRIVEN DSS
provide stakeholders with access to a company’s
intranet and authority or privileges to use specific DSS
capabilities.
8. FUNCTIONS OR SPECIFIC ON GENERAL PURPOSE
help a person to accomplish specific decision task.
help support broad task like project management,
decision analysis and business planning.
7. TYPES OF DSS
9. WEB BASED DSS
is a computerized system that delivers decision
support information or decision support tools to a
manager or business analyst using their client web
browser like internet explorer and mozilla firefox.
8. DSS Characteristics :
⚫Facilitation : DSS facilitate and support specific decision-
making activitiesand/ordecision processes.
⚫Interaction : DSS are computer-based systems designed for
interactive use by decision makers or staff users who control
the sequenceof interaction and theoperations performed.
⚫Ancillary : DSS can support decision makers at any level in
an organization. They are NOT intended to replace decision
makers.
⚫Repeated Use : DSS are intended for repeated use. A
specific DSS may be used routinelyor used as needed forad
hocdecision supporttasks.
⚫Identifiable : DSS may be independentsystems thatcollect
or replicate data from other information systems OR
subsystemsof a larger, more integrated information system.
9. DSS Characteristics (cont.):
⚫Task-oriented : DSS providespecific capabilities that support
one or more tasks related to decision-making, including:
intelligence and data analysis; identification and design of
alternatives; choice among alternatives; and decision
implementation.
⚫Decision Impact : DSS are intended to improve the accuracy,
timeliness, qualityand overall effectiveness of a specific decision
ora set of related decisions.
⚫Supports individual and groupdecision making : It provides a
single platform that allows all users to access the same
information and access the same version of truth, while providing
autonomy to individual users and development groups to design
reporting content locally.
⚫Comprehensive Data Access : Itallows users toaccess data from
different sources concurrently, leaving organizations the freedom
tochoose thedatawarehouse that best suits theirunique
requirementsand preferences.
10. DSS Characteristics (cont.):
⚫Easy to Developand Deploy : DSS deliversan interactive,
scalable platform for rapidly developing and deploying
projects. Multiple projects can be created within a single
shared metadata. Within each project, development teams
createa widevarietyof re-usable metadataobjects.
⚫Integrated software : DSS’s integrated platform enables
administratorsand IT professionals todevelopdata models,
perform sophisticated analysis, generate analytical reports,
and deliverthesereports toend usersviadifferent channels
(Web, email, file, printand mobiledevices).
⚫Flexibility : DSS featuresare flexible and can bealtered
according to need providing a helping hand in the work
process.
11. DSS Objectives :
1. Increase theeffectivenessof the manager's decision-
making process.
2. Supports the manager in thedecision-making process
butdoes not replace it.
3. Improve thedirectors effectivenessof decision
making.
12. DSS Components :
DSS components may beclassified as:
⚫Inputs : Factors, numbers, and characteristics to
analyze.
⚫User Knowledgeand Expertise : Inputs requiring
manual analysis by the user.
⚫Outputs : Transformed data from which DSS
"decisions" aregenerated.
⚫Decisions : Resultsgenerated by the DSS based on
usercriteria.
13. DSS Requirements :
⚫Data collection from multiple sources (sales data,
inventorydata, supplierdata, market research data.
etc.).
⚫Data formatting and collation.
⚫A suitabledatabase locationand format built for
decision support -based reporting and analysis .
⚫Robust toolsand applications toreport, monitor, and
analyze thedata.