Niko's Project Corner

Chess video search engine

Youtube has a quite good search func­tion­al­ity based on video ti­tles, de­scrip­tions and maybe even sub­ti­tles but it doesn't go into ac­tual video con­tents and provide ac­cu­rate times­tamps for users' searches. An youtu­ber "Agad­ma­tor" has a very pop­ular chan­nel (1.1 mil­lion sub­scribers, 454 mil­lion video views at the time of writ­ing) which show­cases ma­jor chess games from past and re­cent tour­na­ments and on­line games. Here a search en­gine is in­tro­duced which an­alyzes the videos, rec­og­nizes chess pieces and builds a database of all of the po­si­tions on the board ready to be searched. It keeps track of the ex­act times­tamps of the videos in which the queried po­si­tion oc­curs so it is able to provide di­rect links to rel­evant videos.

JGit blame for fun and profit(?)

Soft­ware pro­jects are typ­ically "tracked" on a ver­sion con­trol sys­tem (VCS). Each "ver­sion" of the code is called a "com­mit", which does not only store file con­tents, but also plenty of meta­data. This cre­ates a very rich set of data, and in the age of open source there are thou­sands of pro­jects to study. A few ex­am­ples are Git of The­seus and Gi­ten­tial, but by fo­cus­ing on "git blame" (see who has com­mit­ted each line on each file) I hope to bring some­thing new to the table. In short I have an­alyzed how source code gets re­placed by newer code, track­ing the top­ics of who, when and why, and how old the code was.

Benchmarking Elasticsearch and MS SQL on NYC Taxis

The NYC Taxi dataset has been used on quite many bench­marks (for ex­am­ple by Mark Litwintschik), per­haps be­cause it has a quite rich set of columns but their mean­ing is mostly triv­ial to un­der­stand. I de­vel­oped a Clo­jure pro­ject which gen­er­ates Elas­tic­search and SQL queries with three dif­fer­ent tem­plates for fil­ters and four dif­fer­ent tem­plates of ag­gre­ga­tions. This should give a de­cent in­di­ca­tion of these databases per­for­mance un­der a typ­ical work­load, al­though this test did not run queries con­cur­rently and it does not mix dif­fer­ent query types when the bench­mark is run­ning. How­ever bench­marks are al­ways tricky to de­sign and ex­ecute prop­erly so I'm sure there is room for im­prove­ments. In this pro­ject the tested database en­gi­nes were Elas­tic­search 5.2.2 (with Or­acle JVM 1.8.0_121) and MS SQL Server 2014.

Analyzing NYC Taxi dataset with Elasticsearch and Kibana

The NYC taxi­cab dataset has seen lots of love from many data sci­en­tists such as Todd W. Schei­der and Mark Litwintschik. I de­cided to give it a go while learn­ing Clo­jure, as I sus­pected that it might be a good lan­guage for ETL jobs. This ar­ti­cle de­scribes how I loaded the dataset, nor­mal­ized its con­ven­tions and columns, con­verted from CSV to JSON and stored them to Elas­tic­search.

Mustache templates in Clojure

Mus­tache is a well-known tem­plate sys­tem with im­ple­men­ta­tions in most pop­ular lan­guages. At its core it is log­icless same tem­plates can be di­rectly used on other pro­jects. For ex­am­ple I am plan­ning to port this blgo en­gine from PHP to Clo­jure but I only need to re­place La­TeX pars­ing and HTML gen­er­ation parts, I should be able to use ex­ist­ing Mus­tache tem­plates with­out any mod­ifi­ca­tions. To learn Clo­jure pro­gram­ming I de­cided not to use the rec­om­mended li­brary but in­stead im­ple­ment my own.

English hyphenation algorithm in Clojure

This is noth­ing that spec­tac­ular (as if any­thing on my blog is), but I still wanted to de­scribe the out­line of the pro­ject of port­ing the hy­phen­ation al­go­rithm from PHP to Clo­jure. The im­ple­men­ta­tion is only about 80 lines of code + com­ments + 20 lines of unit tests. For com­par­ison the orig­inal PHP abom­ina­tion is about is about 160 LoCs, al­though it is a bit bloated by im­ple­ment­ing the pat­terns search via a trie data struc­ture in­stead of us­ing the str­pos func­tion.