Service Science Textbooks: Opportunities of an Interdisciplinary ApproachDr. Ronny M. Schüritz
With the rise of service science, management and engineering as an independent and interdisciplinary research school, several courses and entire study programs emerged in several universities around the world. Several textbooks address teaching service science from the perspective of a specific discipline such as marketing, operations management or computer science. Therefore, so far teaching service science requires the preparation and combination of lecture material from different textbooks and other teaching material, since there was a lack of interdisciplinary and integrated textbooks for teaching service science. This paper reviews existing service textbooks for motivating the need for an integrated service science textbook. Furthermore, the outline of a new forthcoming interdisciplinary service science textbook is presented. This textbook integrates several disciplines, such as business and economics, quantitative sciences, and computer science. The textbook therefore provides an interdisciplinary map of the world of service science that conquers the challenges to explain service systems to students and practitioners. This enables lecturers to organize their courses along a comprehensive and integrated course concept which has been the result of teaching service science at universities for several years.
Service Science Textbooks: Opportunities of an Interdisciplinary ApproachDr. Ronny M. Schüritz
With the rise of service science, management and engineering as an independent and interdisciplinary research school, several courses and entire study programs emerged in several universities around the world. Several textbooks address teaching service science from the perspective of a specific discipline such as marketing, operations management or computer science. Therefore, so far teaching service science requires the preparation and combination of lecture material from different textbooks and other teaching material, since there was a lack of interdisciplinary and integrated textbooks for teaching service science. This paper reviews existing service textbooks for motivating the need for an integrated service science textbook. Furthermore, the outline of a new forthcoming interdisciplinary service science textbook is presented. This textbook integrates several disciplines, such as business and economics, quantitative sciences, and computer science. The textbook therefore provides an interdisciplinary map of the world of service science that conquers the challenges to explain service systems to students and practitioners. This enables lecturers to organize their courses along a comprehensive and integrated course concept which has been the result of teaching service science at universities for several years.
Introduction to data and text mining - Jisc Digifest 2016Jisc
Text and data mining (TDM) techniques can be applied to a wide range of materials, from published research papers, books and theses, to cultural heritage materials, digitised collections, administrative and management reports and documentation, etc. Use cases include academic research, resource discovery and business intelligence.
This workshop will show the value and benefits of TDM techniques and demonstrate how ContentMine aims to liberate 100,000,000 facts from the scientific literature, and ContentMine will provide a hands on demo on a topical and accessible scientific/medical subject.
The value of Jisc Collections - Jisc Digifest 2016Jisc
What value do we bring to UK institutions through our central negotiations for e-resources?
What value do we bring to UK institutions through our central negotiations for e-resources?
In this session we will provide an overview of what has been achieved so far on behalf of the community, but also look at the key issues we are now addressing, such as debating the limitations of academic journal markets, and the consequences for a transition to open access, as well as how we are working with institutions to build better agreements.
The successful adoption of open access (OA) requires clarity and simplicity in policies and processes. But could there be greater clarity of these to start with? Jisc has been working with experts to help funders and institutions express and develop their policies in a clear and comprehensive format and has developed a schema for this purpose.
This session will explore the schema and how institutions and funders can adopt it and clarify their OA policies.
Research data spring: filling in the digital preservation gapJisc RDM
The research data spring project "Filling in the digital preservation gap" slides for the third sandpit workshop. Project led by Jenny Mitcham at York University and Chris Awre at Hull University.
Call for Papers - International Journal of Data Mining & Knowledge Management...IJDKP
Data mining and knowledge discovery in databases have been attracting a significant amount ofresearch, industry, and media attention of late. There is an urgent need for a new generation ofcomputational theories and tools to assist researchers in extracting useful information from therapidly growing volumes of digital data
Skills for the new generation of statisticians Dario Buono
This presentation analyses the competence profile of official statisticians with a particular focus on new data science competences. Modernization of official statistics will depend on the capability to incorporate new data sources and benefit from “disruptive technologies”. This will require new capabilities, skills and competences that may not be part of the traditional skill set of official statisticians. The document was presented to the Conference of European Statisticians organised at the United Nation in Geneva
International Journal of Data Mining & Knowledge Management Process ( IJDKP )IJDKP
Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data.
International Journal of Data Mining & Knowledge Management Process ( IJDKP )IJDKP
Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data.
This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. 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 following areas, but are not limited to these topics only.
International Journal of Data Mining & Knowledge Management Process ( IJDKP )IJDKP
Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data.
International Journal of Data Mining & Knowledge Management Process ( IJDKP )IJDKP
Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data.
Data Mining & Knowledge Management Process (IJDKP)IJDKP
Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data.
International Journal of Data Mining & Knowledge Management Process ( IJDKP )IJDKP
.
This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. 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 following areas, but are not limited to these topics only.
Topics of interest include, but are not limited to the following
Data mining foundations
Parallel and Distributed Data Mining Algorithms, Data Streams Mining, Graph Mining, Spatial Data Mining, Text video, Multimedia Data Mining, Web Mining,Pre-Processing Techniques, Visualization, Security and Information Hiding in Data Mining
Data mining Applications
Databases, Bioinformatics, Biometrics, Image Analysis, Financial Modeling, Forecasting, Classification, Clustering, Social Networks, Educational Data Mining
Knowledge Processing
Data and Knowledge Representation, Knowledge Discovery Framework and Process, Including Pre- and Post-Processing, Integration of Data Warehousing, OLAP and Data Mining, Integrating Constraints and Knowledge in the KDD Process , Exploring Data Analysis, Inference of Causes, Prediction, Evaluating, Consolidating and Explaining Discovered Knowledge, Statistical Techniques for Generation a Robust, Consistent Data Model, Interactive Data Exploration/Visualization and Discovery, Languages and Interfaces for Data Mining, Mining Trends, Opportunities and Risks, Mining from Low-Quality Information Sources
Important Dates
Submission Deadline : August 23, 2020
Notification : September 23, 2020
Final Manuscript Due : October 01, 2020
Publication Date : Determined by the Editor-in-Chief
Call for Papers
Scope & Topics
Ethics
Archives
Most Cited Articles
Download leaflet
FAQ
ijdkp 10th year logo
Top 10 Cited Papers
From
2011 Volumes
10
Issues
54 Articles
227
Conferences
DMML 2020 - India
DTMN 2020 - Sydney
DMS 2020 - India
DBDM 2020 - Dubai
DMDBS 2020 - India
Courtesy
Smiley face
Data-Enriched Products and Services – Options to Apply Advanced Analytics to ...Dr. Ronny M. Schüritz
With the rise of “big data” and associated technologies, analytics solutions are mushrooming.
Many organizations face an increasing amount of available data, but are still lacking support on how to systematically transform this data into business value. Although there has been progress to apply advanced analytics to improve internal efficiency and effectiveness, the adaptation or creation of more-valuable services – called “Analytics 3.0” by Davenport - is still in its infancy.
In this paper, we try to shed light on the status of the industry and to identify the changes in value propositions that are contributed by analytics. We systematically analyze 700 advanced analytics projects across the industry worldwide via an open coding approach.
First, we derive a series of distinct patterns of how organizations actually create business value – and find that in only 10% of our cases the value proposition is infused by data and analytics. Second, we reveal a set of eleven patterns that illustrate how data and analytics can enrich service offerings – resulting in added value for the customer. Through these patterns we explain how the utilization is altering the service experience, how added value is created for the customer and what data is needed.
Thus, we contribute to the fundamental understanding of how the use of data and the application of analytics may spark service innovation. The identified patterns should give guidance for practitioners on how to utilize (big) data and analytics, not just internally but also for changed value propositions.
Introduction to data and text mining - Jisc Digifest 2016Jisc
Text and data mining (TDM) techniques can be applied to a wide range of materials, from published research papers, books and theses, to cultural heritage materials, digitised collections, administrative and management reports and documentation, etc. Use cases include academic research, resource discovery and business intelligence.
This workshop will show the value and benefits of TDM techniques and demonstrate how ContentMine aims to liberate 100,000,000 facts from the scientific literature, and ContentMine will provide a hands on demo on a topical and accessible scientific/medical subject.
The value of Jisc Collections - Jisc Digifest 2016Jisc
What value do we bring to UK institutions through our central negotiations for e-resources?
What value do we bring to UK institutions through our central negotiations for e-resources?
In this session we will provide an overview of what has been achieved so far on behalf of the community, but also look at the key issues we are now addressing, such as debating the limitations of academic journal markets, and the consequences for a transition to open access, as well as how we are working with institutions to build better agreements.
The successful adoption of open access (OA) requires clarity and simplicity in policies and processes. But could there be greater clarity of these to start with? Jisc has been working with experts to help funders and institutions express and develop their policies in a clear and comprehensive format and has developed a schema for this purpose.
This session will explore the schema and how institutions and funders can adopt it and clarify their OA policies.
Research data spring: filling in the digital preservation gapJisc RDM
The research data spring project "Filling in the digital preservation gap" slides for the third sandpit workshop. Project led by Jenny Mitcham at York University and Chris Awre at Hull University.
Call for Papers - International Journal of Data Mining & Knowledge Management...IJDKP
Data mining and knowledge discovery in databases have been attracting a significant amount ofresearch, industry, and media attention of late. There is an urgent need for a new generation ofcomputational theories and tools to assist researchers in extracting useful information from therapidly growing volumes of digital data
Skills for the new generation of statisticians Dario Buono
This presentation analyses the competence profile of official statisticians with a particular focus on new data science competences. Modernization of official statistics will depend on the capability to incorporate new data sources and benefit from “disruptive technologies”. This will require new capabilities, skills and competences that may not be part of the traditional skill set of official statisticians. The document was presented to the Conference of European Statisticians organised at the United Nation in Geneva
International Journal of Data Mining & Knowledge Management Process ( IJDKP )IJDKP
Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data.
International Journal of Data Mining & Knowledge Management Process ( IJDKP )IJDKP
Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data.
This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. 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 following areas, but are not limited to these topics only.
International Journal of Data Mining & Knowledge Management Process ( IJDKP )IJDKP
Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data.
International Journal of Data Mining & Knowledge Management Process ( IJDKP )IJDKP
Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data.
Data Mining & Knowledge Management Process (IJDKP)IJDKP
Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data.
International Journal of Data Mining & Knowledge Management Process ( IJDKP )IJDKP
.
This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. 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 following areas, but are not limited to these topics only.
Topics of interest include, but are not limited to the following
Data mining foundations
Parallel and Distributed Data Mining Algorithms, Data Streams Mining, Graph Mining, Spatial Data Mining, Text video, Multimedia Data Mining, Web Mining,Pre-Processing Techniques, Visualization, Security and Information Hiding in Data Mining
Data mining Applications
Databases, Bioinformatics, Biometrics, Image Analysis, Financial Modeling, Forecasting, Classification, Clustering, Social Networks, Educational Data Mining
Knowledge Processing
Data and Knowledge Representation, Knowledge Discovery Framework and Process, Including Pre- and Post-Processing, Integration of Data Warehousing, OLAP and Data Mining, Integrating Constraints and Knowledge in the KDD Process , Exploring Data Analysis, Inference of Causes, Prediction, Evaluating, Consolidating and Explaining Discovered Knowledge, Statistical Techniques for Generation a Robust, Consistent Data Model, Interactive Data Exploration/Visualization and Discovery, Languages and Interfaces for Data Mining, Mining Trends, Opportunities and Risks, Mining from Low-Quality Information Sources
Important Dates
Submission Deadline : August 23, 2020
Notification : September 23, 2020
Final Manuscript Due : October 01, 2020
Publication Date : Determined by the Editor-in-Chief
Call for Papers
Scope & Topics
Ethics
Archives
Most Cited Articles
Download leaflet
FAQ
ijdkp 10th year logo
Top 10 Cited Papers
From
2011 Volumes
10
Issues
54 Articles
227
Conferences
DMML 2020 - India
DTMN 2020 - Sydney
DMS 2020 - India
DBDM 2020 - Dubai
DMDBS 2020 - India
Courtesy
Smiley face
Data-Enriched Products and Services – Options to Apply Advanced Analytics to ...Dr. Ronny M. Schüritz
With the rise of “big data” and associated technologies, analytics solutions are mushrooming.
Many organizations face an increasing amount of available data, but are still lacking support on how to systematically transform this data into business value. Although there has been progress to apply advanced analytics to improve internal efficiency and effectiveness, the adaptation or creation of more-valuable services – called “Analytics 3.0” by Davenport - is still in its infancy.
In this paper, we try to shed light on the status of the industry and to identify the changes in value propositions that are contributed by analytics. We systematically analyze 700 advanced analytics projects across the industry worldwide via an open coding approach.
First, we derive a series of distinct patterns of how organizations actually create business value – and find that in only 10% of our cases the value proposition is infused by data and analytics. Second, we reveal a set of eleven patterns that illustrate how data and analytics can enrich service offerings – resulting in added value for the customer. Through these patterns we explain how the utilization is altering the service experience, how added value is created for the customer and what data is needed.
Thus, we contribute to the fundamental understanding of how the use of data and the application of analytics may spark service innovation. The identified patterns should give guidance for practitioners on how to utilize (big) data and analytics, not just internally but also for changed value propositions.
RD shared services and research data springJisc RDM
Daniela Duca's presentation at the DataVault workshop on 29 June. An overview of research at risk, research data shared service and research data spring.
Ken Chad Ken Chad Consulting
In 2017 the Economist magazine, in a much quoted article said, ”the world’s most valuable resource is no longer oil, but data. Smartphones and the internet have made data abundant, ubiquitous and far more valuable”. While data may be abundant, in the world of libraries, publishers and intermediaries it is typically siloed and the value and potential to improve services has barely begun to be realised. Ken will argue that, on their own, data from libraries, publishers or conventional intermediaries will not be enough to deliver the kinds of predictive analytics and Artificial Intelligence (AI) solutions that are emerging. Commercial companies and sector bodies like Jisc have begun to develop platforms that make use of data from a variety of sources. This will be an intensely competitive environment and it is not yet clear who the winners will be for, as Indian Prime Minister Narendra Modi said at the world economic forum in January 2018, ‘whoever controls data will have control over the world in the future’. The data wars have begun...
In 2017 the Economist magazine, in a much quoted article said, ‘the world’s most valuable resource is no longer oil, but data. Smartphones and the internet have made data abundant, ubiquitous and far more valuable”. While data may be abundant, in the world of libraries, publishers and intermediaries it is typically siloed and the value and potential to improve services has barely begun to be realised. On their own, data from libraries, publishers or conventional intermediaries will not be enough to deliver the kinds of predictive analytics and Artificial Intelligence (AI) solutions that emerging. Commercial companies and sector bodies like Jisc have begun to develop platforms that make use of data from a variety of sources. This will be an intensely competitive environment and it is not yet clear who the winners will be for, as Indian Prime Minister Narendra Modi said at the world economic
This presentation was held by Professor Christine Legner (HEC Lausanne) at the Swiss Day on November 8, 2017, in Lausanne, Switzerland. It addresses the need for organisations to think about data and its management in new ways, as many corporations engage in the digital and data-driven transformation of their business. It concludes with three recommendations: 1) assess data's business value and impact, 2) measure and improve data quality, and 3) democratize data and support data citizenship.
Presentation given by Sarah Jones and Joy Davidson to a group of South African librarians at a webinar organised by LIASA HELIG. http://www.liasa.org.za/node/977
SciVal offers quick, easy access to the research performance of 8,500 research institutions and 220 nations worldwide. A ready-to-use solution with unparalleled power and flexibility, SciVal enables you to visualize research performance, benchmark relative to peers, develop collaborative partnerships and analyze research trends.
Supporting Research Data Management in UK Universities: the Jisc Managing Res...L Molloy
Research data management in the UK: interventions by the Jisc Managing Research Data programme and the Digital Curation Centre. Specifies the importance of academic librarians for RDM. Includes links to openly available training resources. Presentation by L Molloy to ExLibris event, 'Excellence in Academic Knowledge Management', Utrecht, 29 October 2013.
What infrastructure is necessary for successful research data management (RDM...heila1
RDM life cycle; research data elements in the research life cycle; what is RDM infrastructure; IT infrastructure; Library infrastructure; Research Office infrastructure; Examples of 4 universities RDM service offerings
Data Catalogues - Architecting for Collaboration & Self-ServiceDATAVERSITY
The interest in Data Catalogs is growing as more business & technical users are looking to gain insight from data using a self-service approach. Architectural techniques for Data Provisioning and Metadata Cataloging have evolved to cater to these new audiences and ways of working. This webinar provides concrete methods of architecting your Self-service BI & Analytics environment to foster collaboration while at the same time maintaining Data Quality and reducing risk.
Learning Resources Week 2
Frankfort-Nachmias, C., & Leon-Guerrero, A. (2020). Social statistics for a diverse society (9th ed.). Sage Publications.
· Chapter 2, “The Organization and Graphic Presentation Data” (pp. 27-74)
Wagner, III, W. E. (2020). Using IBM® SPSS® statistics for research methods and social science statistics (7th ed.). Thousand Oaks, CA: Sage Publications.
· Chapter 5, “Charts and Graphs”
· Chapter 11, “Editing Output”
Walden University Writing Center. (n.d.). General guidance on data displays. Retrieved from http://waldenwritingcenter.blogspot.com/2013/02/general-guidance-on-data-displays.html
Use this website to guide you as you provide appropriate APA formatting and citations for data displays.
Laureate Education (Producer). (2016j). Visual displays of data [Video file]. Baltimore, MD: Author.
Note: The approximate length of this media piece is 9 minutes.
In this media program, Dr. Matt Jones discusses frequency distributions. Focus on how his explanation might support your analysis in this week’s Assignment. (video attached separately)
sustainability
Case Report
Integrated Understanding of Big Data, Big Data
Analysis, and Business Intelligence: A Case Study
of Logistics
Dong-Hui Jin and Hyun-Jung Kim *
Seoul Business School, aSSIST, 46 Ewhayeodae 2-gil, Seodaemun-gu, Seoul 03767, Korea; [email protected]
* Correspondence: [email protected]; Tel.: +82-70-7012-2722
Received: 5 October 2018; Accepted: 17 October 2018; Published: 19 October 2018
����������
�������
Abstract: Efficient decision making based on business intelligence (BI) is essential to ensure
competitiveness for sustainable growth. The rapid development of information and communication
technology has made collection and analysis of big data essential, resulting in a considerable increase
in academic studies on big data and big data analysis (BDA). However, many of these studies are
not linked to BI, as companies do not understand and utilize the concepts in an integrated way.
Therefore, the purpose of this study is twofold. First, we review the literature on BI, big data,
and BDA to show that they are not separate methods but an integrated decision support system.
Second, we explore how businesses use big data and BDA practically in conjunction with BI through
a case study of sorting and logistics processing of a typical courier enterprise. We focus on the
company’s cost efficiency as regards to data collection, data analysis/simulation, and the results from
actual application. Our findings may enable companies to achieve management efficiency by utilizing
big data through efficient BI without investing in additional infrastructure. It could also give them
indirect experience, thereby reducing trial and error in order to maintain or increase competitiveness.
Keywords: business application; big data; big data analysis; business intelligence; logistics;
courier service
1. Introduction
A growing number of corporations depend on various and cont.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
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
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Adjusting OpenMP PageRank : SHORT REPORT / NOTESSubhajit Sahu
For massive graphs that fit in RAM, but not in GPU memory, it is possible to take
advantage of a shared memory system with multiple CPUs, each with multiple cores, to
accelerate pagerank computation. If the NUMA architecture of the system is properly taken
into account with good vertex partitioning, the speedup can be significant. To take steps in
this direction, experiments are conducted to implement pagerank in OpenMP using two
different approaches, uniform and hybrid. The uniform approach runs all primitives required
for pagerank in OpenMP mode (with multiple threads). On the other hand, the hybrid
approach runs certain primitives in sequential mode (i.e., sumAt, multiply).
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
1. KIT – The Research University in the Helmholtz Association
KARLSRUHE SERVICE RESEARCH INSTITUTE (KSRI)
www.kit.edu
Exploring the Value of Data
A Research Agenda
Tobias Enders
IESS, September 2018
2. Karlsruhe Service Research Institute
www.ksri.kit.edu
The value of data assets plays a significant role in various
business transactions
23/09/20182
Microsoft acquired LinkedIn (2016) Facebook acquired WhatsApp (2014)
§ Transaction volume was 26.2 bn USD
§ Price far beyond the sum of tangible and
intangible assets on the balance sheet
§ Due to the massive amounts of user data held
by LinkedIn
§ Transaction volume of 19 bn USD
§ WhatsApp only employed 55 people at the
time of the transaction
§ Access to 450 million active users
Sources:
Microsoft (2016)
Welt (2014)
3. Karlsruhe Service Research Institute
www.ksri.kit.edu
As data being generated grows rapidly, organizations search
for ways to value their data assets
23/09/20183
0
10000
20000
30000
40000
2005 2010 2012 2015 2020
Estimated worldwide data by 2020
Datavolumeinexabyte
Reasons to evaluate data:
1
2
3
Direct data monetization
Internal investment
M&A
Establishing KPIs allows better planning, controlling and adjusting
Finance Customers Processes Data
?e.g. EBIT e.g. CLV e.g. Cycle
Time
Sources:
Turner et al. (2014)
Akred and Samani (2018)
4. Karlsruhe Service Research Institute
www.ksri.kit.edu
The literature review will provide an overview of the
perspectives taken onto the topic of data value
20/09/20184
How can organizations value data as a strategic asset?
The objective of the literature review is to develop an understanding
of the research streams around the value of data.
5. Karlsruhe Service Research Institute
www.ksri.kit.edu
Methodology: a structured literature research yielded 28
papers that address data value
20/09/20185
Step 1 Step 2 Step 3
Databases
§ AISeL
§ ScienceDirect
§ Scopus
§ EBSCO
Search on
§ Title, Abstract, Key Words
§ Key words: value AND data
Exclude
§ Tutorials, workshops,
abstracts-only, panels, blogs
Exclude papers that…
§ Look at value from a non-
economic perspective
§ Deal with IT investments in
general
§ Have a strong technical
focus
§ Have a strong focus on non-
profit organizations
Include
§ Forward search
§ Backward search
245 Papers 23 Papers 28 Papers
Source:
Webster and Watson (2002)
6. Karlsruhe Service Research Institute
www.ksri.kit.edu
Results 1: The majority of research targets business value
derived from data while little attention is given to the source
20/09/20186
28
Papers
15 papers focus on realizing
business value 13 papers target data
management topics
7. Karlsruhe Service Research Institute
www.ksri.kit.edu
Results 2: Three research streams have been identified that
broach the topic of data value
23/09/20187
Insight
Value
Value
Data Value Chain
3
2
Information
ValueData
Value
1
Business
Value
Research Gap
No conclusive research has been conducted on the data source itself –
independent of knowing all potential use cases.
Sources:
Abbasi et al. (2016)
Gao et al. (2015)
Seddon et al. (2017)
8. Karlsruhe Service Research Institute
www.ksri.kit.edu
To further explore data value, a four-step approach is proposed
How can organizations determine the value of their data assets and how does that impact
their data management activities?
20/09/20188
Research Question
Criteria influencing the value of data
Clustering of data sources
Data Governance
Value Contribution
Modelvalidation
4
3
2
1
9. Karlsruhe Service Research Institute
www.ksri.kit.edu
Understanding value drivers for data lets organizations cluster
their assets by value
Influencing Criteria
§ Current research suggests that
certain criteria may have an
influence on data value (e.g. re-
usability and the value decay over
time, data source, data quality)
23/09/20189
2
4
3
1
1
2
Clustering
§ Clusters may help focusing
investments on data (e.g. data
management, data acquisition)
Sources:
Otto (2011)
Malgieri and Custers (2018)
Mamonov and Triantoro (2018)
10. Karlsruhe Service Research Institute
www.ksri.kit.edu
Introducing value-based governance guidelines allows
organizations to better manage and protect high-value assets
23/09/201810
3
4
Data Governance
§ Value-based management of data
§ May apply to guidelines such as
encryption, storage, access
management
Value Contribution
§ Allows comparison to organizational
non-data assets
§ Validate the results of initial studies
2
4
3
1
11. Karlsruhe Service Research Institute
www.ksri.kit.edu
Limitations and future research
23/09/201811
§ With the proposed research
agenda, further research into the
field of data value is enabled
§ In addition to an internal view on
data value, an external view may be
taken into account
§ Proprietary knowledge may be
available in the industry only and
not available in public literature
§ Different approach of clustering the
relevant papers
Limitations Future Research
12. Karlsruhe Service Research Institute
www.ksri.kit.edu
Karlsruhe Service Research Institute (KSRI)
Kaiserstr. 89 | Building 05.20
D-76133 Karlsruhe
@ksri_kit
www.ksri.kit.edu
Thank you –
please feel free to reach out!
20/09/2018
tobias.enders@kit.edu
Tobias Enders
Karlsruhe Service Research Institute
https://www.linkedin.com/company/karls
ruhe-service-research-institute-ksri-/
13. Karlsruhe Service Research Institute
www.ksri.kit.edu
References
Microsoft: Microsoft to acquire LinkedIn,
https://news.microsoft.com/2016/06/13/microsoft-to-acquire-linkedin/.
Facebook kauft WhatsApp für 19 Milliarden Dollar
https://www.welt.de/wirtschaft/article125021667/Facebook-kauft-WhatsApp-fuer-19-Milliarden-
Dollar.html.
Turner, V., Gantz J., Reinsel D., and S.M., 2014. The digital universe of opportunities: Rich
data and the increasing value of the internet of things. IDC / EMC Report, p.17.
Akred, J., Samani, A.: Your Data Is Worth More Than You Think,
https://sloanreview.mit.edu/article/your-data-is-worth-more-than-you-think/, last accessed
2018/02/20.
Malgieri, G., Custers, B.: Pricing privacy - the right to know the value of your personal data.
Comput. Law Secur. Rev. 34, 289–303 (2018).
Mamonov, S., Triantoro, T.M.: The strategic value of data resources in emergent industries.
Int. J. Inf. Manage. 39, 146–155 (2018).
Otto, B.: Data Governance. Bus. Inf. Syst. Eng. 3, 241–244 (2011).
23/09/201813
[1]
[2]
[3]
[4]
[5]
[6]
[7]