Big Data becomes Big Analysis
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• Improve Data Management with Semantic Data Integration • Discuss the issues of data variety and data uncertainty • Moving from Big Data to Big Analysis • How to apply Analysis to Big Data (Big Analysis) • Benefits of Advanced Analytics in Life Science
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• Big Data technologies, especially Data Lakes are spreading across many industries at the moment with the hopes that they will provide unprecedented capabilities for data integration and data analytics
• In spite of the popularity and promise of these technology approaches, many early adopters are not seeking immediate solutions to their complex problems. Answers are not simply appearing – this talk will explore this issue more thoroughly
• Of the 4 V’s of Big Data, Data Variety and Data Veracity (uncertainty) are of increasing importance. These can cause barriers to successful integration strategies , which, in turn, can lead to poorly performing analytics.
• The problems of Variety and Veracity can be tackled using a new form of Data Science which combines formal ontologies with statistical heuristics. This talk will explore some key features of these approaches and how they can be developed together in symbiosis – leading to complex models that allow for improved analytics – or as we call it Big Analysis.
• The end result is improved capture of data types/sources, from laboratory instrument data, to clinical data, to regulatory rules & submissions, all the way to business drivers for the enterprise. In the end providing advanced analytics capabilities that can be built as modules and expand across an enterprise.
Ab cs of big data
The white paper discusses how enterprises are facing exponentially growing amounts of data that is breaking down traditional storage architectures. It outlines NetApp's approach to addressing big data challenges through what it calls the "Big Data ABCs" - analytics, bandwidth, and content. This allows customers to gain insights from massive data sets, move data quickly for high-performance applications, and store large amounts of content for long periods without increasing complexity. NetApp provides solutions to help enterprises take advantage of big data and turn it into business value.
Data minig with Big data analysis
This document discusses data mining with big data. It begins with an agenda that covers problem definition, objectives, literature review, algorithms, existing systems, advantages, disadvantages, big data characteristics, challenges, tools, and applications. It then goes on to define the problem, objectives, provide a literature review summarizing several papers, and describe the architecture, algorithms, existing systems, HACE theorem that models big data characteristics, advantages of the proposed system, challenges, and characteristics of big data. It concludes that formalizing big data analysis processes will be important as data volumes continue increasing.
Overview of mit sloan case study on ge data and analytics initiative titled g...
GE collects sensor data from industrial equipment to analyze equipment performance and predict failures. It created a "data lake" to integrate raw flight data from 3.4 million flights with other data sources. This allows data scientists to identify issues reducing equipment uptime for customers. However, GE faces challenges in finding qualified analytics talent and establishing effective data governance as it scales its data and analytics efforts.
Data lifecycle mgt across the enterprise
This talk will provide a means to discuss the capture, integration and dissemination of data across large enterprises. We will show how data variety is continuing to grow, meaning new data sources are steadily becoming available for use in analysis. Data veracity is also of importance since a large amount of data is fuzzy (uncertain) in nature. The ability to integrate these various data sources and provide improved capabilities to understand and use it is of increasing importance in today’s pharma climate. We call this Reference Master Data Management (RMDM).
This talk will span an arc of data lifecycle management, beginning with instrument data, moving across to clinical studies, production, regulatory affairs and finally e-archiving (see Fig. 1). I will show how these systems can use a common semantics for modeling of important metadata, which can apply the FAIR principles of Findability, Accessibility, Interoperability and Reusability to a common “semantic hub” that can connect data sources of different varieties across the enterprise. ADF files, for example, use their Data Description layer to provide semantic metadata about file contents. Similarly, semantics can be used to describe clinical trials data, regulatory data, etc., through to archiving, for improved storage and search over long periods of time.
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Reinventing Laboratory Data To Be Bigger, Smarter & Faster
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Overview of mit sloan case study on ge data and analytics initiative titled g...
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More from OSTHUS
The Fast Track to Fair Lab Data
Current labs can greatly benefit from a digital transformation.
FAIR data principles are crucial in this process.
Laying a solid data governance foundation is an invaluable long-term move.
Challenges & Opportunities of Implementation FAIR in Life Sciences
Speak in common terms – identify Business Outcomes (value) as well as technology
Don’t say “semantics”, “FAIR”, “ontologies”, etc. – talk about outcomes and results
Drive projects through results – QUICK WINS
Identify the right data – build off of that (evolution not revolution)
Think about legacy systems, provenance, governance, stewardship, etc. – have answers to the nay-sayers.
Be honest what this will do and what it won’t
ROI – have this in mind (Business Value not Tech Value)
Cost savings (reduced hours, faster search, accurate reporting, better visibility, etc.)
Risk Mitigation (improved regulatory, corporate knowledge vs. indivual, M&A, etc.)
Innovation (what is the value to being a thought leader?)
From allotrope to reference master data management
We will present the updated Allotrope framework and cover .adf files and how they are used. We’ll demonstrate semantic modeling in .adf (OWL models + the SHACL constraint language). We’ll show how the data description layer in .adf can be extended via a “semantic hub” that we call Reference Master Data Management, which can be used across the enterprise. RMDM provides a means to integrate metadata about any data source within your enterprise – including structured, semi-structured and unstructured data. Customer examples from current project work will be given where possible. Last we’ll show scalability of this approach using data science techniques can be employed beyond just the metadata – we refer to this as Big Analysis.
Revolutionizing Laboratory Instrument Data for the Pharmaceutical Industry:...
The Allotrope Foundation is a consortium of major pharmaceutical companies and a partner network whose goal is to address challenges in the pharmaceutical industry by providing a set of public, non-proprietary standards for using and integrating analytical laboratory data. Current challenges in data management within the pharmaceutical industry often center around inconsistent or incomplete data and metadata and proprietary data formats. Because of a lack of standardization, several operations (e.g. integration of instruments/applications, transfer of methods or results, archiving for regulatory purposes) require unnecessary efforts. Further, higher level aggregation of data, e.g. regulatory filings, that are derived from multiple sources of laboratory data are costly to create. These unnecessary costs impact operations within a company’s laboratories, between partnering companies, and between a company and contract research organizations (CROs). Finally, the accelerating transition of laboratories from hybrid (paper + electronic) to purely electronic data streams, coupled with an ever-increasing regulatory scrutiny of electronic data management practices, further require a comprehensive solution. This talk will discuss how The Allotrope Foundation is providing a new framework for data standards through collaboration between numerous stakeholders.
Demystifying Semantics:Practical Utilization of Semantic Technologies for Rea...
In our webinar on Jan 17th, 2017, Eric and Heiner gave attendees insights on the following:
1. What semantics are (model/data separation, graphs, apply better meaning to data, etc.)
2. Why you should consider using these technologies (real world examples of benefits our customers are seeing)
3. How to pick the right tech for your needs (provide a description of the types of graph/RDF stores out there – we have a matrix based on features – and show how various SPARQL queries work against legacy data.)
Why paperless lab is just the first step towards a smart lab
Life science’s main asset is its data. Data forms the basis of scientific decision making and its availability via electronic systems is a prerequisite for collaborative work and successful innovation. While more data is published as linked (open) data, huge amounts of data remain unused in internal data silos, such as various ELN’s, because of substantial integration efforts and data quality issues. Since the overwhelming amount of data is unstructured, information extraction and corresponding classification and semantic labeling of content is required. To generate value from your ELN data, a solid informatics strategy is needed to ensure data quality and streamline analytics. Semantic technologies are key enabler to overcome existing limitations.
Allotrope foundation vanderwall_and_little_bio_it_world_2016
The document discusses the Allotrope Foundation's efforts to drive improved data modeling and management in life sciences research through the use of semantic technologies. It outlines current challenges with data silos and lack of standards. The Foundation is developing the Allotrope Data Format and taxonomies to standardize metadata and facilitate data integration and sharing. Several pharmaceutical companies are now implementing the framework in areas like small molecule CMC and biotherapeutics development. The Foundation aims to enable smarter laboratories of the future with integrated, sharable, and analyzable data.
Semantics for Integrated Analytical Laboratory Processes – the Allotrope Pers...
The software environment currently found in the analytical community consists of a patchwork of incompatible software, proprietary and non-standardized file formats, which is further complicated by incomplete, inconsistent and potentially inaccurate metadata. To overcome these issues, Allotrope Foundation is developing a comprehensive and innovative framework consisting of metadata dictionaries, data standards, and class libraries for managing analytical data throughout its life cycle. In this talk we describe how laboratory data and semantic metadata descriptions are brought together to ease the management of a vast amount of data that underpins almost every aspect of drug discovery and development.
Semantics for integrated laboratory analytical processes - The Allotrope Pers...
The software environment currently found in the analytical community consists of a patchwork of incompatible software, proprietary and non-standardized file formats,
which is further complicated by incomplete, inconsistent and potentially inaccurate metadata. To overcome these issues, the Allotrope Foundation develops a
comprehensive and innovative Framework consisting of metadata dictionaries, data standards, and class libraries for managing analytical data throughout its lifecycle. The
talk describes how laboratory data and their semantic metadata descriptions are brought together to ease the management of vast amount of data that underpin almost
every aspect of drug discovery and development.
Best Practice Reference Architecture for Data Curation
OSTHUS representative Michael Engels presented "Best Practice Reference Architecture for Data Curation" at BioIT World 2015.
Allotrope Foundation & OSTHUS at SmartLab Exchange 2015: Update on the Allotr...
During SmartLab Exchange 2015, Allotrope Foundation and OSTHUS presented the latest update on the Allotrope Framework. To learn more, please view the slides below.
Presented by:
Dana Vanderwall, BMS Research IT & Automation Patrick Chin, Merck Research Laboratories IT Wolfgang Colsman, OSTHUS
OSTHUS-Allotrope presents "Laboratory Informatics Strategy" at SmartLab 2015
Building your laboratory informatics strategy: The benefit of reference architectures & data standardization.
Presented by:
Wolfgang Colsman, OSTHUS
Dana Vanderwall, Bristol-Myers Squibb
More from OSTHUS (14)
Challenges & Opportunities of Implementation FAIR in Life Sciences
Challenges & Opportunities of Implementation FAIR in Life Sciences
From allotrope to reference master data management
From allotrope to reference master data management
Revolutionizing Laboratory Instrument Data for the Pharmaceutical Industry:...
Revolutionizing Laboratory Instrument Data for the Pharmaceutical Industry:...
Demystifying Semantics:Practical Utilization of Semantic Technologies for Rea...
Demystifying Semantics:Practical Utilization of Semantic Technologies for Rea...
Why paperless lab is just the first step towards a smart lab
Why paperless lab is just the first step towards a smart lab
Allotrope foundation vanderwall_and_little_bio_it_world_2016
Allotrope foundation vanderwall_and_little_bio_it_world_2016
Semantics for Integrated Analytical Laboratory Processes – the Allotrope Pers...
Semantics for Integrated Analytical Laboratory Processes – the Allotrope Pers...
Semantics for integrated laboratory analytical processes - The Allotrope Pers...
Semantics for integrated laboratory analytical processes - The Allotrope Pers...
Best Practice Reference Architecture for Data Curation
Best Practice Reference Architecture for Data Curation
Allotrope Foundation & OSTHUS at SmartLab Exchange 2015: Update on the Allotr...
Allotrope Foundation & OSTHUS at SmartLab Exchange 2015: Update on the Allotr...
OSTHUS-Allotrope presents "Laboratory Informatics Strategy" at SmartLab 2015
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一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
毕业原版【微信:176555708】【(GWU,GW毕业证书)乔治·华盛顿大学毕业证】【微信:176555708】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
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留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Population Growth in Bataan: The effects of population growth around rural pl...
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Media monitoring in veterinary medicien
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
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
The Building Blocks of QuestDB, a Time Series Database
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
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
My burning issue is homelessness K.C.M.O.
My burning issue is homelessness in Kansas City, MO
To: Tom Tresser
From: Roger Warren
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
by
Timothy Spann
Principal Developer Advocate
https://budapestdata.hu/2024/en/
https://budapestml.hu/2024/en/
tim.spann@zilliz.com
https://www.linkedin.com/in/timothyspann/
https://x.com/paasdev
https://github.com/tspannhw
https://www.youtube.com/@flank-stack
milvus
vector database
gen ai
generative ai
deep learning
machine learning
apache nifi
apache pulsar
apache kafka
apache flink
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
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四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
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◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
Learn SQL from basic queries to Advance queries
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
Experts live - Improving user adoption with AI
Bekijk de slides van onze sessie Enhancing Modern Workplace Efficiency op Experts Live 2024.
Predictably Improve Your B2B Tech Company's Performance by Leveraging Data
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
University of New South Wales degree offer diploma Transcript
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06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Dat...
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.
一比一原版(Harvard毕业证书)哈佛大学毕业证如何办理
毕业原版【微信:41543339】【(Harvard毕业证书)哈佛大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版(牛布毕业证书)牛津布鲁克斯大学毕业证如何办理
毕业原版【微信:41543339】【(牛布毕业证书)牛津布鲁克斯大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（永久存档真实可查）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路），我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
→ 【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...
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End-to-end pipeline agility - Berlin Buzzwords 2024
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
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Big Data becomes Big Analysis
- 1. Big Data Becomes Big Analysis Eric Little, PhD Chief Data Officer eric.little@osthus.com
- 2. Slide 2 The Current Situation in the Pharma Industry Many challenges exist for data to be captured, integrated and shared Data Silos Incompatible instruments and software systems, proprietary data formats Legacy architectures are brittle and rigid SME knowledge resides in people’s heads, little common vocabulary Data schemas are not explicitly understood Lack of common vision between business units and scientists 2 How do we change this landscape?
- 3. Slide 3 Pharma Is An Example of One Industry that Must Adapt “It's better to be a pirate than to join the Navy.” ―Steve Jobs There normally exists a persistent desire to look to past success and anchor ourselves to it Following preconceived doctrines is not always what’s best Apple changed telecommunications as a computer company What will the future of technology hold? Whatever it is – will require an adventurous approach
- 4. Slide 4 Moving to Smart Data Smart data can be added to existing systems Does not require replacement of existing tech Smart data provides a separation of: Model Layer Data Layer Link to the model layer Leave data in place Smart data links information from the models to instance-level data
- 6. Slide 6 Enter Big Data Hypothesis: If I have more data at my fingertips – then I will have more answers This is not necessarily the case. One major hurdle: “Real-world data […] is messy data, filled with inconsistencies, potential biases, and noise.” Copping & Li Harvard Business Review Nov 29, 2016
- 7. Slide 7 Understanding the 4V’s of Big Data Normally the focus – Big Data Analysis is more than just size Performance is Critical to Success Data complexity is increasing – Model complexity Uncertainty abounds – requires statistics and probabilities Majority of Big Data analytics approaches treat these two V’s Semantic technologies provide clear advantages Mathematical Clustering Techniques provide clear advantages
- 8. Slide 8 The power of analytics is now just beginning to be felt Moore’s Law pertaining to processing is not the problem Focus on the growth of Analysis: From 1988-2003 Computer processing speed grew by 1000x In the same period algorithm dev grew by 43,000x Advanced analytics is reaching an inflection point in adoption by both mid-market organizations and large enterprises in an effort to gain a competitive advantage. The Growth of Analytics is Changing the Game ANALYTICS International Institute for Analytics Jan 6, 2015
- 9. Slide 9 THE MOVE FROM BIG DATA TO BIG ANALYSIS STATISTICAL SEMANTICS MACHINE LEARNING REASONING
- 10. Slide 10 Big Analysis Requires Hybrid Architectures Semantic DBs Unstructured Docs Structured Data Cloud DBs (NoSQL)Analytics Dashboards & Reports Integration Layer
- 11. Slide 11 1. Data Lakes Lightweight metadata provides search Addresses problem of “schema on read” 2. Data Catalogs Vocabs, Taxonomies, Ontologies Links private & public data 3. Advanced Analytics Text extraction – combines statistics and semantics Classifiers inside of algorithms can be uniform Trends, clusters can be labeled as “named graphs” The WHAT (content), WHO (users) & HOW (workflows) can all be captured and used. Use Cases • Small Molecule • Large molecule • Crop Sciences • Regulatory Intelligence • Archiving
- 12. Slide 12 Innovation is key The Role of Innovation: Requires foresight and stepping out of your comfort zone Today’s problems will not be tomorrow’s problems – so we need new approaches Cannot be “business as usual” because the landscape is changing Be outside the box and reward creativity
- 13. Thank You For More Information: www.biganalysis.com eric.little@osthus.com Twitter: @OntoEric @OSTHUS