PowerStream: Propelling Energy Innovation with Predictive Analytics

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Spark Summit 2016 talk by Nikita Shamgunov (MemSQL)

Data & Analytics

(c) Nikita Shamgunov and MemSQL

Topics
• Renewable Energy
• PowerStream
• Demo
• Q&A
(c) Nikita Shamgunov and MemSQL

Renewable Energy
in the News
(c) Nikita Shamgunov and MemSQL

Germany Just Got Almost All of
Its Power From Renewable Energy
May 15, 2016
Bloomberg: h,p://www.bloomberg.com/news/ar5cles/2016-05-16/germany-just-got-almost-all-of-its-
power-from-renewable-energy
(c) Nikita Shamgunov and MemSQL

Investment in renewables
reached $286 billion worldwide
in 2015
BBC: h&p://www.bbc.com/news/science-environment-36420750
(c) Nikita Shamgunov and MemSQL

Introducing
PowerStream
(c) Nikita Shamgunov and MemSQL

MemSQL PowerStream
Predic'ng the global health of wind turbines
(c) Nikita Shamgunov and MemSQL

(c) Nikita Shamgunov and MemSQL

(c) Nikita Shamgunov and MemSQL

(c) Nikita Shamgunov and MemSQL

Streamliner Architecture for PowerStream
(c) Nikita Shamgunov and MemSQL

(c) Nikita Shamgunov and MemSQL

(c) Nikita Shamgunov and MemSQL

Demo Sequence
• Part I
• High Speed Ingest
• Predic3ve Analy3cs
• Business Intelligence
• Part II
• Spark SQL Pushdowns
(c) Nikita Shamgunov and MemSQL

MemSQL and Spark
Real-&me applica&on
Demo part I
(c) Nikita Shamgunov and MemSQL

(c) Nikita Shamgunov and MemSQL

(c) Nikita Shamgunov and MemSQL

(c) Nikita Shamgunov and MemSQL

(c) Nikita Shamgunov and MemSQL

Demo Part I
(c) Nikita Shamgunov and MemSQL

MemSQL and Spark
Be#er together
Demo part II
(c) Nikita Shamgunov and MemSQL

(c) Nikita Shamgunov and MemSQL

SQL Pushdown
• Delegate SQL query processing to a database
• Enhance speed and concurrency
• Complement other Spark capabili=es
• Use Spark as a high level interface
• Command line example with and without pushdown
(c) Nikita Shamgunov and MemSQL

Demo Part II
(c) Nikita Shamgunov and MemSQL

From a Real-Time Dashboard to Predic5ve
Applica5ons
(c) Nikita Shamgunov and MemSQL

(c) Nikita Shamgunov and MemSQL

Ques%ons and answers
(c) Nikita Shamgunov and MemSQL

(c) Nikita Shamgunov and MemSQL

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Here we present a general supervised framework for record deduplication and author-disambiguation via Spark. This work differentiates itself by – Application of Databricks and AWS makes this a scalable implementation. Compute resources are comparably lower than traditional legacy technology using big boxes 24/7. Scalability is crucial as Elsevier’s Scopus data, the biggest scientific abstract repository, covers roughly 250 million authorships from 70 million abstracts covering a few hundred years. – We create a fingerprint for each content by deep learning and/or word2vec algorithms to expedite pairwise similarity calculation. These encoders substantially reduce compute time while maintaining semantic similarity (unlike traditional TFIDF or predefined taxonomies). We will briefly discuss how to optimize word2vec training with high parallelization. Moreover, we show how these encoders can be used to derive a standard representation for all our entities namely such as documents, authors, users, journals, etc. This standard representation can simplify the recommendation problem into a pairwise similarity search and hence it can offer a basic recommender for cross-product applications where we may not have a dedicate recommender engine designed. – Traditional author-disambiguation or record deduplication algorithms are batch-processing with small to no training data. However, we have roughly 25 million authorships that are manually curated or corrected upon user feedback. Hence, it is crucial to maintain historical profiles and hence we have developed a machine learning implementation to deal with data streams and process them in mini batches or one document at a time. We will discuss how to measure the accuracy of such a system, how to tune it and how to process the raw data of pairwise similarity function into final clusters. Lessons learned from this talk can help all sort of companies where they want to integrate their data or deduplicate their user/customer/product databases.

Deduplication and Author-Disambiguation of Streaming Records via Supervised M...

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The use of large-scale machine learning and data mining methods is becoming ubiquitous in many application domains ranging from business intelligence and bioinformatics to self-driving cars. These methods heavily rely on matrix computations, and it is hence critical to make these computations scalable and efficient. These matrix computations are often complex and involve multiple steps that need to be optimized and sequenced properly for efficient execution. This work presents new efficient and scalable matrix processing and optimization techniques based on Spark. The proposed techniques estimate the sparsity of intermediate matrix-computation results and optimize communication costs. An evaluation plan generator for complex matrix computations is introduced as well as a distributed plan optimizer that exploits dynamic cost-based analysis and rule-based heuristics The result of a matrix operation will often serve as an input to another matrix operation, thus defining the matrix data dependencies within a matrix program. The matrix query plan generator produces query execution plans that minimize memory usage and communication overhead by partitioning the matrix based on the data dependencies in the execution plan. We implemented the proposed matrix techniques inside the Spark SQL, and optimize the matrix execution plan based on Spark SQL Catalyst. We conduct case studies on a series of ML models and matrix computations with special features on different datasets. These are PageRank, GNMF, BFGS, sparse matrix chain multiplications, and a biological data analysis. The open-source library ScaLAPACK and the array-based database SciDB are used for performance evaluation. Our experiments are performed on six real-world datasets are: social network data ( e.g., soc-pokec, cit-Patents, LiveJournal), Twitter2010, Netflix recommendation data, and 1000 Genomes Project sample. Experiments demonstrate that our proposed techniques achieve up to an order-of-magnitude performance.

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In my capstone project, I investigated the impact of COVID-19 on education. Using data analysis and statistical methods, I explored various aspects such as enrollment trends, access to resources, and socioeconomic disparities. I found a significant association between children missing classes and a lack of internet connection at home, as well as between household financial situations and children's enrollment in school. These findings highlight the importance of addressing disparities in internet access, household finances, and geographical location to ensure equal educational opportunities for all students during and beyond the pandemic.

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Explore the cutting-edge methods and technologies utilized in rain forecasting, from traditional meteorological models to machine learning algorithms. Discover how these predictive tools enable accurate anticipation of rainfall patterns, aiding in disaster preparedness, agriculture planning, and urban infrastructure management. To learn in detail about analysis and prediction visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/

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