Real estate project – Minutes from kickoff

On Monday the 21st of November, we kicked off our first bigdata.be project using historic sales data from a real estate website  (meetup).

Interesting discussions throughout the evening lead us to define two paralel tracks. On the one hand, we will try to come up with a semi-structured model for real estate data. On the other hand, we will attempt to apply data analytics on real estate data using algorithms provided by the Apache Mahout community.

Besides being of interest to the bigdata.be community, we reasoned that a semi-structured data model would support integration of real estate data with orthogonal information derived from e.g. OpenStreetMap or OpenBelgium. This will enable us to enrich the existing data with things like

  • distance to n cities,
  • a ‘green index’ that correlates how far a real estate property is located from a nearby forest, or
  • a ‘density index’ that ties in with the number of houses that are for sale at the moment in a radius of 1, 2, 4, 8 or 16 km .

We thought of using HBase or Cassandra as datastores to address this task, but we will only decide this during follow-up meetings. Remembering the interests poll from the first bigdata.be meetup, there will hopefully be quite a few members from the bigdata.be community to elaborate this track of the real estate project.

The second track of the real estate project on the other hand aims to attract social interest by producing insights that are more relevant to a wider audience then just our bigdata.be community. As such we touched three topics for data analytics we could elaborate depending on their feasibility.

  1. Prediction of the price and time of the sale
    Based on archives from real estate companies, we will evaluate how well the price and time of the sale of a house can be predicted. For the suspicious readers, have a look a the Zestimate® price from the zillow.com real estate agency from the U.S..
  2. Text mining on free text descriptions
    It’s obvious that a plastic door correlates to a cheap house, while a granite kitchen correlates to expensive houses. But what other vocabulary-based associations can we derive by performing text mining analysis on the free text fields specified by the seller? (cfr. Freakonomics chapter 2)
  3. Recommendation engine for similar real estate properties
    Finally, by analyzing traffic logs from a real estate website, we should be able to build a recommendation engine that guides the visitor’s to related houses on sale. Think of how you search Amazon.co.uk for a Canon 550D, and you will surely see the camera bag as well.

 

Keep your eyes on the bigdata.be meetup site and, if interested, join the next events on the real estate project!

 

2nd Meetup – BigData project

On Wednesday August 24th 2011, we had our second meetup. As some people cancelled at the last moment, the crowd was not as large as for our first meetup: now, there were just 6 of us.

This meetup was the first time we organised our get-together as an open discussion. Davy Suvee came up with that idea and apparently everybody present enjoyed this format very much. This article is a synopsis of the topics discussed.

On the look for a Big Data project

One of the major demands from our community members is to work together on some specific Big Data project to gain hands on experience. In this respect the Big Data Wars thread was started on our groups page. However, instead of organizing a true public challenge, it would be easier and more instructive to just participate on a challenge as a team or to look for a specific project that we can implement ourselves. They are outlined underneath.

We decided to create a bitbucket account where we can define, plan, work and code on these projects.

Wikipedia’s Participation Challenge

A few days after Daan Gerits launched the BigData Wars idea on the group page, Nathan Bijnens referred to the Wikipedia’s Participation Challenge as a good fit for a BigData.be project. You can read the full description on the kaggle.com page, but the idea is to build a predictive model that allows the Wikimedia Foundation to understand what factors determine editing behaviour and to forecast long term trends in the number of edits to be expected on wikipedia.org. A random sample from the English Wikipedia dataset from the period January 2001 – August 2010 serves as training dataset for the predictive model.

Appealing to this challenge is that it is a very specific problem description,  encompassing quite a large fraction of the big data complexity:

  • data availability,
  • datastore modelling,
  • defining secondary data,
  • deriving sampled test data,
  • building predictive models using various approaches, technologies and algorithms.

However, as the deadline for the project is very close, i.e. Tuesday 20 September 2011, we probably won’t be truly participating, but we can still use the problem definition.

So, if you are interested in participating in this topic, contact us or join in in the discussion or the repository:

 

Other proposals

Two more proposals were made as project topics:

  1. BioMed: As a computational biology researcher at Ghent University, Kenny Helsens proposed to see if he could come up with some suitable project definition in the area of bioinformatics, maybe genome- or proteome related.
  2. Immo and GIS: Another interesting area for a well-suited project might be the combination of historical data made available by some immo website with GIS related data e.g. from OpenStreetMap. A number of interesting problems can be derived, requiring e.g. predictive models. We’ll be contacting some immo websites on this matter.

As these projects are still in their incubation stage, we haven’t yet created any specific web areas for them. However, these might follow very soon, so keep checking back!

Realtime MapReduce

Daan Gerits has been working on some big data and noSQL projects, and was wondering if anybody has experience with bringing Hadoop and/or MR to the realtime playing field, instead of keeping it strictly for batch processing. The basic difference being that you would be able to feed the data crunching algorithms incrementally or by streaming data into the system and have the algorithms merge these into the already (partially) existing result sets.

During a presentation at the SAI on 7 April 2011, Steven Noels and Wim Van Leuven also pointed out that any big data processing system needs a combination of a batch layer with a speed layer to achieve at least eventual accuracy. The speed layer architectually being the most challenging. However, if we could combine realtime with existing MR algorithms, …

Some suitable technologies and pointers where raised: Yahoo S4, IBM InfoSphere Streams, Datastax Brisk, and Twitter’s Storm. However, no practical experience exists within our community.

Big Data Technology poll

As our meetup group was rather small, we did not redo the technology poll. However, we were wondering if some suitable tool exist to automate the poll via the bigdata.be website or other electronic means, like Google docs. So if you have any good idea in this area, please let us know!

See y’all at the next meetup!

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