Summary 1.15.7 References 1.16 preface 1.17 ## Build Web Application with Golang ## Purpose Because I'm interested in web application development, I used my free time to write this book as an open applications. - For those of you who are working with PHP/Python/Ruby, you will learn how to build a web application with Go. - For those of you who are working with C/C++, you will know how the web works. I believe binary file of our package. How do we use it? Obviously, we need to create a new application to use it. Create a new application package called mathapp. cd $GOPATH/src mkdir mathapp cd mathapp vim main.go

## +24 ## Application of C++ in Computational Cancer Modeling RUIBO ZHANG 20 24 September 15 - 20 ## Application of C++ in Cancer Modeling • The speaker (Ruibo) • University of Washington, department

## CS 591 K1: Data Stream Processing and Analytics Spring 2020 ## 1 /23: Stream Processing Fundamentals Vasiliki (Vasia) Kalavri vkalavri@bu.edu ## What is a stream? - In traditional data processing processing applications, we know the entire dataset in advance, e.g. tables stored in a database. A data stream is a data set that is produced incrementally over time, rather than being available in full before high-volume, real-time data that might be unbounded • we cannot store the entire stream in an accessible way • we have to process stream elements on-the-fly using limited memory ## Properties of data streams

Applications with Open Application Model (OAM) and Dapr Mark Russinovich Chief Technology Officer, Microsoft Azure @markrussinovich ## Application models Describes the topology of your application and its components their application to interact with other services and data stores  Open Application Model (OAM) Open Application Model Platform agnostic application model Distributed Application Runtime (Dapr) dapr Building blocks for building scalable distributed apps ![Image](/uploads/documents/b/c/2/9/bc29b5fbf1de02f8968a900b84d384e5/p6_2

### RANGE.IO ## Why NativeScript Demands You Rethink Your Mobile Application Strategy Nick Van Weerdenburg Founder, Rangle.io 11:33 AM All Cities ### KIVA RANGLE.IO https://github.com/rangle/kiva-demo Evaporation ## ↑↑ ↑↑ ## THINGS TO CONSIDER What's The User Model? 1. The boundary of the application a. Does it cross over into website, store, other applications, conversation... b. Or is it new

## Scalable Stream Processing - Spark Streaming and Flink Amir H. Payberah payberah@kth.se 05/10/2018 https://id2221kth.github.io ## Data Processing Graph Data Pregel, GraphLab, PowerGraph GraphX GraphX, X-Stream, Chaos Batch Data MapReduce, Dryad FlumeJava, Spark Structured Data Spark SQL Machine Learning Mliib Tensorflow Streaming Data Storm, SEEP, Naiad, Spark Streaming, Flink, Millwheel BigTable, Cassandra ## Distributed Messaging Systems Kafka Resource Management Mesos. YARN ## Stream Processing Systems Design Issues ▶ Continuous vs. micro-batch processing Record-at-a-Time vs. declarative

# CS 591 K1: Data Stream Processing and Analytics Spring 2020 ## 1 /28: Stream ingestion and pub/sub systems Vasiliki (Vasia) Kalavri vkalavri@bu.edu ## Streaming sources  Where do stream processors read data from? Files, e.g. transaction logs Sockets IoT devices and sensors Databases and KV stores Message queues unpredictable delays • might be producing too fast • stream processor needs to keep up and not shed load • might be producing too slow or become idle • stream processor should be able to make progress • might

2023 第三届中国 Rust 开发者大会 6.17-6.18 @Shanghai ## Use Rust to Develop the Decentralized Open Data Application Mike Tang daogangtang@gmail.com @daogangtang 2023-06-08 ## | 互联网的终局 ## 这是一个什么时代？ → 裁员 → will increase quickly on any dimension. → Multiple dimensions ## I A New Proposal: Open Data Application ## The Road to Open Web  ## EightFish

Stream Processing with Apache RocketMQ and Apache Flink 王鑫 · The Apache Software Foundation Nov.4, 2018, Shanghai, Apache Flink China Meetup ![Image](/uploads/documents/5/1/f/3/51f363b7213b31d1c40f613be3dd1945/p1_1 system experience • Love open source & community • Focus on distributed technologies, especially stream processing • https://github.com/vesense ![Image](/uploads/documents/5/1/f/3/51f363b7213b31d1c40f613be3dd1945/p2_2

# CS 591 K1: Data Stream Processing and Analytics Spring 2020 4/16: Skew mitigation Vasiliki (Vasia) Kalavri vkalavri@bu.edu ## Key partitioning  > δ*N, where N is the number of stream elements • The solution will not contain any item y with frequency: \\delta^{\*}N $|| ## Notation (I) Input: a stream of items N: number of items in the stream $ f_{e} $ : true frequency of the item e in the input stream f: estimated frequency of item δ: user-defined