Machine Data for the Masses
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Websummit 2016 b1nate.i0 presentation - With agile development processes transforming software creation and deployment, they are delivering these new services at record speed for business. The outcomes? For starters: new market wealth and tremendous competitive pressures for traditional businesses trying to make the shift.
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如果您在英、加、美、澳、欧洲等留学过程中或回国后：
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3、不清楚认证流程以及材料该如何准备；
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留信认证的作用：
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2. 留信认证对就业的好处
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特殊原因导致无法毕业，也可以联系我们帮您办理相关材料：
１：在魁北克大学挂科了，不想读了，成绩不理想怎么办？？？
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3：回国了找工作没有魁北克大学文凭怎么办？有本科却要求硕士又怎么办？
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如果您在英、加、美、澳、欧洲等留学过程中或回国后：
1、在校期间因各种原因未能顺利毕业《UQAM成绩单工艺详解》【Q/WeChat：1954292140】《Buy Université du Québec à Montréal Transcript快速办理魁北克大学教育部学历认证书毕业文凭证书》，拿不到官方毕业证；
2、面对父母的压力，希望尽快拿到；
3、不清楚认证流程以及材料该如何准备；
4、回国时间很长，忘记办理；
5、回国马上就要找工作《正式成绩单魁北克大学学历认证失败怎么办》【q薇1954292140】《成绩单水印UQAM成绩单如何办理》办给用人单位看；
6、企事业单位必须要求办理的；
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3、真实使馆认证（即留学人员回国证明），使馆存档可通过大使馆查询确认.
4、留信网认证，国家专业人才认证中心颁发入库证书，留信网存档可查.
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留信认证的作用：
1. 身份认证：留信认证可以证明你的留学经历是真实的，且你获得的学历或学位是正规且经过认证的。这对于一些用人单位来说，尤其是对留学经历有高度要求的公司（如跨国公司或国内高端公司），这是非常重要的一个凭证。
专业评定：留信认证不仅认证你的学位证书，还会对你的所学专业进行评定。这有助于展示你的学术背景，特别是对于国内公司而言，能够清楚了解你所学专业的水平和价值。
国家人才库入库：认证后，你的信息将被纳入国家人才库，并且可以在国家人才网等平台上展示，供包括500强公司等大型公司挑选和聘用人才。这对于回国找工作特别是进入大公司，具有非常积极的作用。
2. 留信认证对就业的好处
提高竞争力：通过留信认证，尤其是对你所学专业的认证，可以大大提高你在国内求职时的竞争力。许多公司对留学生背景和所学专业有很高的要求，认证后的信息能够帮助公司快速识别符合条件的候选人。
增强信任度：公司往往会对没有认证的学历背景产生疑虑，而留信认证为你的学历背景提供了第三方权威机构的背书，增强了雇主的信任。原版复刻加拿大多伦多大学成绩单(UTSG毕业证书) 文凭
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如果您在英、加、美、澳、欧洲等留学过程中或回国后：
1、在校期间因各种原因未能顺利毕业《UTSG成绩单工艺详解》【Q/WeChat：1954292140】《Buy St. George campus Transcript快速办理多伦多大学教育部学历认证书毕业文凭证书》，拿不到官方毕业证；
2、面对父母的压力，希望尽快拿到；
3、不清楚认证流程以及材料该如何准备；
4、回国时间很长，忘记办理；
5、回国马上就要找工作《正式成绩单多伦多大学毕业证书》【q薇1954292140】《购买毕业证UTSG成绩单定购》办给用人单位看；
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国家人才库入库：认证后，你的信息将被纳入国家人才库，并且可以在国家人才网等平台上展示，供包括500强公司等大型公司挑选和聘用人才。这对于回国找工作特别是进入大公司，具有非常积极的作用。Valkey 101 - SCaLE 22x March 2025 Stokes.pdf
Valkey 101 - SCaLE 22x March 2025 Stokes.pdfDave Stokes
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An Introduction to Valkey, Presented March 2025 at the Southern California Linux Expo, Pasadena CA. Valkey is a replacement for Redis and is a very fast in memory database, used to caches and other low latency applications. Valkey is open-source software and very fast.
the data analytics process:Real life applications
the data analytics process:Real life applicationsjhanvisaxena30
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the data analytics process: Real life applications
Deep-QPP: A Pairwise Interaction-based Deep Learning Model for Supervised Que...
Deep-QPP: A Pairwise Interaction-based Deep Learning Model for Supervised Que...suchanadatta3
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Motivated by the recent success of end-to-end deep neural models
for ranking tasks, we present here a supervised end-to-end neural
approach for query performance prediction (QPP). In contrast to
unsupervised approaches that rely on various statistics of document
score distributions, our approach is entirely data-driven. Further,
in contrast to weakly supervised approaches, our method also does
not rely on the outputs from different QPP estimators. In particular, our model leverages information from the semantic interactions between the terms of a query and those in the top-documents retrieved with it. The architecture of the model comprises multiple layers of 2D convolution filters followed by a feed-forward layer of parameters. Experiments on standard test collections demonstrate
that our proposed supervised approach outperforms other state-of-the-art supervised and unsupervised approaches.The truth behind the numbers: spotting statistical misuse.pptx
The truth behind the numbers: spotting statistical misuse.pptxandyprosser3
?
As a producer of official statistics, being able to define what misinformation means in relation to data and statistics is so important to us.
For our sixth webinar, we explored how we handle statistical misuse especially in the media. We were also joined by speakers from the Office for Statistics Regulation (OSR) to explain how they play an important role in investigating and challenging the misuse of statistics across government.2025-03-03-Philly-AAAI-GoodData-Build Secure RAG Apps With Open LLM
2025-03-03-Philly-AAAI-GoodData-Build Secure RAG Apps With Open LLMTimothy Spann
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2025-03-03-Philly-AAAI-GoodData-Build Secure RAG Apps With Open LLM
https://aaai.org/conference/aaai/aaai-25/workshop-list/#ws14
Developing a Music Distribution Plan for Bri La Pelúa
Developing a Music Distribution Plan for Bri La Pelúairramos8843
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Music distribution plan for Bri"La Pelúa"Optimizing Common Table Expressions in Apache Hive with Calcite
Optimizing Common Table Expressions in Apache Hive with CalciteStamatis Zampetakis
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In many real-world queries, certain expressions may appear multiple times, requiring repeated computations to construct the final result. These recurring computations, known as common table expressions (CTEs), can be explicitly defined in SQL queries using the WITH clause or implicitly derived through transformation rules. Identifying and leveraging CTEs is essential for reducing the cost of executing complex queries and is a critical component of modern data management systems.
Apache Hive, a SQL-based data management system, provides powerful mechanisms to detect and exploit CTEs through heuristic and cost-based optimization techniques.
This talk delves into the internals of Hive's planner, focusing on its integration with Apache Calcite for CTE optimization. We will begin with a high-level overview of Hive's planner architecture and its reliance on Calcite in various planning phases. The discussion will then shift to the CTE rewriting phase, highlighting key Calcite concepts and demonstrating how they are employed to optimize CTEs effectively.Machine Data for the Masses
- 2. WHO I AM Co-Founder & CTO, Sumo Logic Cloud-based Machine Data Analytics Service Applications, Operations, Security Chief Architect, ArcSight Major SIEM player in the enterprise space Log Management for security and compliance
- 3. AGENDA Paradox 1 Nach dem Spiel ist immer vor dem Spiel Paradox 2 Processing data creates exponentially more data Conclusion Machine Data is Continuous Intelligence
- 9. OPTIMIZATION
- 10. PARADOX 1 Nach dem Spiel ist immer vor dem Spiel “After the game is before the game”
- 11. SOFTWARE IS EATING THE WORLD
- 12. DATA IS EATING YOUR BUSINESS
- 13. Data is growing 2X YoY
- 17. MACHINE DATA Data generated by machines (d’oh!) Machine Data is data that was produced entirely by machines, or data that is more about observing humans than recording their choices Curt Monash, Database Analyst Guru
- 18. MACHINE DATA Data generated by machines (d’oh!) Logs, CDR, Trades, Telemetry, Security Events
- 20. MACHINE DATA Data generated by machines (d’oh!) Logs, CDR, Trades, Network & Security Events Poly-structured, instructions not included Comes at you in real-time A lot of it, and growing exponentially
- 22. PARADOX 2 Processing data creates exponentially more data
- 24. THIS IS PRODUCTION YOU ARE HERE ? CEO / Board / Shareholders ? Customers ? Partners ? Customer Success ? CSO / VP Security ? Product Management App Log App Monitoring Infra Monitoring Ad-hoc Tools Cloud Management Infra Log Security Tools Compliance Reporting Dev Tools Modern Application OpsDevOps LOBSecurity
- 25. MACHINE DATA IN 3 DIMENSIONS Data Use Case Actor
- 27. Complex Fault & Root Cause Analysis REACTIVE How Long Until Problem Identified & Resolved?
- 28. Watch What Happens Live PROACTIVE How Long Until You Know You Have A Problem?
- 29. Attempt To Prevent Bad Things PREDICTIVE How Can We Not Have A Problem At All?
- 32. 1,200+ ENTERPRISE CUSTOMERS WITH OVER 10,000 USERS Financial & Prof. Services Travel SaaS & Cloud Media & Entertainment Retail, Industrials & Mfg. Gov. & Education Tech. & Communications Healthcare As of March 2016
- 33. SUMMARY Machine Data is Continuous Intelligence @raychaser