Summary of CSE 519 --- Lecture 23: Achieving Scale (Fall 2021)

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00:00:00 - 01:00:00

The lecture covers the different aspects of big data, including the problems that can arise from bias in data sets. The instructor also discusses how to properly sample data so that it is representative of the population as a whole.

  • 00:00:00 In this lecture, CSE Professor John Hart explains how to study for the final exam, which will be held on December 14th. The final exam consists of 12 short answer problems, and students are allowed to have two sheets of scratch paper.
  • 00:05:00 The CSE 519 exam will be a written short answer type question, and you are not allowed to use calculators or notes. If you lose connection, you should try to log back in.
  • 00:10:00 The lecture discusses the logistics of the course, including the final project and peer grading.
  • 00:15:00 In this lecture, Professor Daron has students think about the different ways in which big data can be a problem. He goes on to discuss velocity, volume, and variety when talking about big data. Finally, he provides a brief overview of what a big data table looks like.
  • 00:20:00 The speaker discusses the drawbacks of using big data sets, which typically arise as a result of bias in who is using the data. One way to overcome bias is to use surveys, which can provide accurate information. However, Twitter data is less reliable because it is biased towards people who are more likely to be on Twitter.
  • 00:25:00 The lecture discussed the problem of bias in data sets and how it can be mitigated by analyzing data over a period of time. The talk also touched on the importance of sentiment analysis in order to understand public opinion.
  • 00:30:00 In this lecture, the instructor discusses the benefits and drawbacks of having big data. One advantage of big data is that it can be used to train many models, but the downside is that it can be difficult to analyze individual data sets. Filtering out data that is harder to analyze is important when working with big data.
  • 00:35:00 In this lecture, the instructor discusses how to sample data from a large dataset in a principled way. The simplest way to do this is to take the first k rows of the data, but this has biases due to the order in which the records appear in the input file. Another way to sample from a large dataset is to sort the data by a certain criterion, such as social security number. If the data is sorted in a particular way, there are likely to be biases due to the way the data was sorted.
  • 00:40:00 Random sampling can lead to bias in the results of a poll because it is not reproducible.
  • 00:45:00 In sampling, it is important to consider how to sample the population so that each group of people sampled is representative of the population as a whole. One way to do this is to randomly sample the population, but this is often impractical. Stratified random sampling is a more practical way to sample, where groups of people are sampled based on their demographics.
  • 00:50:00 The problem with stream sampling is that if we don't start filling in a sample as we go along, we risk not including any of the tweets in our sample. This can be solved by randomly selecting a number of tweets from the stream and including them in the sample, without knowing in advance how many tweets will be in the sample.
  • 00:55:00 In this lecture, the probability of an element surviving a sampling process is explained using the example of a stream of tweets. It is shown that a clever online sample can be assembled using a statistically rigorous algorithm, and that data parallelism can be used to speed up the process of moving and combining data sets. Finally, the advantages and disadvantages of cloud computing are discussed.

01:00:00 - 01:20:00

In this lecture, CSE Professor Michael Stone discusses the concepts behind the Hadoop file system and the final exam. The practice final is scheduled for Friday, December 14th, and will consist of two questions. Study materials for the final exam are available on Piazza. The video discusses the CSE 519 lecture, "Achieving Scale." The instructor encourages students to try out the practice final in order to debug any problems with their projects.

  • 01:00:00 In order to use a large number of machines, an infrastructure must be in place that can coordinate the large number of machines. Strange things happen when you have large numbers of machines, and processes become more complex. One example is how social gatherings change as you have larger numbers of people involved. If you want to throw a party, for example, you would need to identify a leader. If not, it would be chaos. If you are having a wedding with a hundred people in it, you would need to plan for the possibility that three people will die during the course of the day.
  • 01:05:00 Mapreduce programming is used to spread work among multiple machines, hashing values to a particular location so that all tweets mentioning the entity will be stored at that location.
  • 01:10:00 In this lecture, the instructor describes how MapReduce works and how it can be used to aggregate data. The reduced phase of MapReduce is responsible for counting the number of times a given key is referenced in a set of data.
  • 01:15:00 In this lecture, CSE Professor Michael Stone discusses the concepts behind the Hadoop file system and the final exam. The practice final is scheduled for Friday, December 14th, and will consist of two questions. Study materials for the final exam are available on Piazza.
  • 01:20:00 The video discusses the CSE 519 lecture, "Achieving Scale." The instructor encourages students to try out the practice final in order to debug any problems with their projects.

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