The Boston Hack/Reductions

On June 25th Hack/Reduce coders took over the NERD center in Cambridge. It was the biggest Hack/Reduce yet with 90 coders hacking on big data projects on a total of 600 nodes provided by Hopper and Cloudant.

As always, many were humbled by crashing bits of code but that was quickly forgotten while frantically reloading a white screen with some percentages representing your job being processed. An admittedly weird way to spend your Saturday, but you just can’t say no to the opportunity of having the power of hundreds of machines at your fingertips to serve your hastily crafted code.

In the end we let all the participants vote for the best Hack/Reduction, the winner was team 6 whose application visualized words that were most related to a chosen word. The related words were based on an algorithm that they created to calculate related words by processing all of wikipedia . We used polleverywhere from Boston for the voting, it worked great!

Results for Hack/Reduce Boston

The presentations

Team 1 Merging twitter and music data

Team: Pete Kruskall, Cinjon Resnick, Greg Sabo, Thierry Bertin-Mahieux , Bruce spang, Rob Speer, Thor Kell, William Dvorak, Nadav Aharony

Team 1 worked on a couple of different projects with the Million Song Dataset. They created nice looking visualizations of clusters of Musicians by merging the Million Song Dataset and the Twitter social graph dataset.  They also calculated the similarity between twitter handles of musicians based on who they followed and created nice dynamic visualizations of the relationships.

Team 2 How book mentions correlates to stock prices.

Team: Ben Popp, Adam Buggia, Andrew Rollins, Michael Axiak, Manish Maheshwari

Team 2 wanted to calculate how mentions in books correlates to stock prices. They used Google n-grams and NYSE stock data.

The results? “nobody’s gona get rich” (That’s a quote from their presentation, not our evaluation.)

Team 3 – Location popularity in history based on book mentions.

Matt Veitas, Alex Harris, Grace Woo, Pablo Azar, Justin Ryan

Team 3 wanted to see how popular locations have been in literature starting way back in 1700. They did this by calculating the amount of mentions in books for every location. The jobs were still running when we had the presentations so we didn’t get to see the final results… Great effort though from this team that hadn’t worked with Hadoop before.

Team 4 – How much is Tom Cruise worth for a movie?

Tuan Phan, Ekaterina Lesnaia, Jason Nochlin

Team 4 wanted to estimate the value of Tom Cruise by analyzing the IMDB dataset and the value an actor has on the gross sales of a movie. I think the results said that Tom was worth at least 1.6 Million for a movie, which means that the studios might have to rethink the salaries they’re paying.

Team 5 – Federal election donation clusters

Eric Brown-Munoz, Jacob Elder, Ben Darfler, Jim Gammill, Van Simmons

Team 5 wanted to match and cluster campaign donators from federal election data. We feared some government officials might turn up but it all ended ok. It turned out that merging the different types of names used in the dataset to one name was difficult and no politically sensitive results were achieved.

Team 6 – Winner! Clustering related words from wikipedia

Satish Gopalakrishnan, Vineet Manohar

Satish and Vineet wanted to create an application that would find a list of associated words for any chosen word. They created a distance algorithm that ranked words based on how close to the original word they were mentioned in wikipedia articles.  To get the results, they scanned through the wikipedia dataset and looked for the associated words for “McCain”, “Erlang” and “Reebok”.
winning team presenting

Team 7 – EnglishCentral – Which are the most difficult words for english learners?

JM Van Thong , Don McAllaster, Jonathon Marston

Team 7 wanted to analyze a dataset from EnglishCentral to find the words that English learners from specific countries have the most trouble with in spoken language. The dataset had 60 million recordings of learners learning English.

The most challenging word for Japanese English learners was the word “really”.

Next the team wants to find the 100 most difficult words per country for learners from around the world.

The team also discussed that it was very useful to learn how you had to break one task in to small chunks in order to be able to process it with Hadoop.

Team 8 – mappers for freebase dataset

Tom Morris
Tom Morris created mappers for freebase to make it easier at future Hack/Reduce events to use the freebase dataset. Thanks Tom!

Team 9 – Quant Finance

Dhanvi Reddy, Alban Chevignard, Ajit Padukone, Kah Keng Tay

Team 9 wanted to try MapReduce for quant finance. They basically created different portfolio strategies based on historical performance that they could then evaluate. The process they had to go through was to calculate:

  1. Monthly returns from daily prices for all stocks
  2. Create a model from monthly returns (a forecast of returns & risk)
  3. Create and test portfolio weights based on the model created
  4. Analyzing the portfolio return

The portfolio strategies tested were different versions based on historical performance.

Until next time…

We want to thank all of the participants for an amazing event, see you soon again! We also want to thank the sponsors, Hopper and Cloudant and Microsoft for offering us the space!

Then next Hack/Reduce will be organized right after summer, stay tuned!



284 thoughts on “The Boston Hack/Reductions”

  1. Pingback: visit usmore here
  2. Pingback: east african art
  3. Pingback: hcg diet protocol
  4. Pingback: Visit site
  5. Pingback: Home
  6. Pingback: colon cleansing
  7. Pingback: muchas noticias
  8. Pingback: Marisha Carda
  9. Pingback: hotel in tokyo
  10. Pingback: belly fat diet
  11. Pingback: e cig
  12. Pingback: e cig reviews
  13. Pingback: optymalizacja
  14. Pingback: credit check
  15. Pingback: more info
  16. Pingback: arima onsen ryokan
  17. Pingback: dr oz and garcinia
  18. Pingback: rower
  19. Pingback: switch 5 port
  20. Pingback: vapor cigarettes
  21. Pingback: Outdoor
  22. Pingback: informacion veraz
  23. Pingback: Austin SEO Expert
  24. Pingback: house alarms
  25. Pingback: floor plan layout
  26. Pingback:
  27. Pingback:
  28. Pingback: beauty tips
  29. Pingback:
  30. Pingback: My Site
  31. Pingback: blog de noticias
  32. Pingback: conmanai dgrasdd
  33. Pingback: fraud watch
  34. Pingback: potencja
  35. Pingback: antler spray
  36. Pingback: sam waltonman
  37. Pingback: vladislav davidzon
  38. Pingback: Trackback
  39. Pingback: mailbox yellow
  40. Pingback: Trackback
  41. Pingback: iherb coupon
  42. Pingback: iherb coupon code
  43. Pingback: iherb promo code
  44. Pingback: kenosha dentists
  45. Pingback: hackntool
  46. Pingback: Friv Games
  47. Pingback: buy cialis
  48. Pingback: Trackback
  49. Pingback: cialis avis
  50. Pingback: check it out
  51. Pingback: 2014 ios hacks
  52. Pingback: links
  53. Pingback: seks contact in
  54. Pingback: Soccer Fundraiser
  55. Pingback: Start
  56. Pingback: plombier paris 2
  57. Pingback: plombier paris
  58. Pingback: Advogado familia
  59. Pingback: tenant screening
  60. Pingback: Health
  61. Pingback: graphic design
  62. Pingback: Finance
  63. Pingback: Liberty
  64. Pingback: cheap cigarettes
  65. Pingback: Trading
  66. Pingback: mark cobb
  67. Pingback: kolikkopelit
  68. Pingback: hairy anus
  69. Pingback: water ionizers
  70. Pingback: US gold
  71. Pingback: iherb coupon
  72. Pingback: Business
  73. Pingback: workout obsession
  74. Pingback: whole foods
  75. Pingback: click here
  76. Pingback: iherb coupon
  77. Pingback: iherb coupon
  78. Pingback: Camp
  79. Pingback: stara cegla
  80. Pingback: serial key
  81. Pingback: nice 06
  82. Pingback: product key
  83. Pingback: Opzioni Binarie
  84. Pingback: Technology
  85. Pingback: School
  86. Pingback: Primerica scam
  87. Pingback: abstract paintings
  88. Pingback: Diindolylmethane
  89. Pingback: Clothing
  90. Pingback: water ionizer
  91. Pingback: adelGAZA20
  92. Pingback: part time jobs
  93. Pingback: visit us
  94. Pingback: global travel
  95. Pingback: Silver Buyers
  96. Pingback: kratom wiki
  97. Pingback: Financial
  98. Pingback: Wczasy nad morzem
  99. Pingback: webcam
  100. Pingback: ballsy media
  101. Pingback: auto news
  102. Pingback: Core Products
  103. Pingback: wpc home
  104. Pingback: Advertisements
  105. Pingback: Click here
  106. Pingback: this contact form
  107. Pingback: vitamin c serum 20
  108. Pingback: boczniak
  109. Pingback: kangen
  110. Pingback:
  111. Pingback: bhw
  112. Pingback: help moppy
  113. Pingback: beautiful women
  114. Pingback: Gifts
  115. Pingback: laser lipo
  116. Pingback: surveys for money
  117. Pingback: lampshades
  118. Pingback: astroloji
  119. Pingback: M88
  120. Pingback: Gelesis Smart Pill
  121. Pingback: harmen tall
  122. Pingback: birmingham hotels
  123. Pingback: acné quistico
  124. Pingback: canola
  125. Pingback: metabolic cooking
  126. Pingback: Party Shop

Comments are closed.