Visualization using d3.js and DataMaps to visualize attack within given network. Application take data from is IBM QRadar and with help of RESTful API sent query into d3.js when it geocodes to lat-ling and place into the map. For the different level of attack points and lines colorized and filtered on front-end.

For the current visualization we use:

• HTML5 for general page layout,
• OpenStreetMap for basic map layer,
•  GeoJSON for target the province borders on representing map,
• CSS3 for visual color and general styling,
• JavaScript for hover effects. 

With existed world map we create layer with GeoJSON encoding China provinces (including Taiwan). Next, we create additional layer with data, associated with country/province from base map layer. Then colorize the chloroplast to get the visual color distribution across the map. Also added labels for basic view and additional hover effect with corresponding province/data on it.

The main goal to get simillal functionality from Vizabi library in NVD3.
For front-end purpose we use Angular.js and modify original NVD3 code to reach:

• Develop selects that changes the input parameters of the axes for graph.
• Develop changing time scale.
• Play button on the timeline.
• Approaching similarity with Vizabi in displaying data on a chart Bar chart.
• Ability to define the thickness of the lines on the chart Line chart.

Task:

Develop d3 graph to display paid / unpaid work for men / women depending on the country / countries selected.

Solution:

We used NWD3 collection of components for d3.js, added responsiveness and cross-browser support.

Task:

Build a classifier that can find the localization site of a protein in yeast, based on 8 attributes (features).

Solution:

For performing classification there was constructed a 3-layer artifical neural network (ANN) and specially a feed-forward multilayer perception. We have used a stochastic gradient descent with back-propagation to train our ANN.

Task:

Build simple web solution that displays realtime data using Cubism.js with Google Big Query database as datasource.

Solution:

We have built simple authorization, API for working with BigQuery, front-end controller for managing multiple graphs and simple static cubism.js graphs.

Task:

Build data scrapper from the certain website and export data to .csv.

Solution:

We used Scrapy framework for extracting the data from websites.

Task:

Develop d3 graph to display paid / unpaid work for men / women depending on the country / countries selected.

Solution:

We used NWD3 collection of components for d3.js, added responsiveness and cross-browser support.

Task:

Create a reference implementation of Spark MLLib in churn modeling project.

Solution:

We used Mahout Random Forest Classification and Spark MLLib Random Forest Regressions to predict the probability of churn of customers for online-shop.

Task:

Develop Prediction Model for webspam and hyperlink analysis designed and trained (with provided data) to achieve certain prediction goals.

Solution:

We have built model for Spam\Nonspam prediction for links analysis company. We have use Big Data methods for input data size 70+ Gb. There were a lot of text features, which were preprocessed by using TF-IDF, Word2Vec and Features Selecting methods. The columns with date format were changed to timestamp format, and period of page life was extracted. As result we have the percentage prediction for each class: Nospam, Page Spam, Domain Spam.

Task:

Build a simulator for inner use which predicts label of time series data.

Solution:

We created a console script written on R and Bash for production purposes that validates predictive models in specific iterative customer defined way. It comprises iterative data splitting, teaching model, predicting outcomes and evaluation of the model performance.

Task:

Develop a classifier to identify characters in captchas. Image preprocessing, training, classification had to be done in Python using standard libraries like OpenCV, Scikit-Image, Scikit Learn, etc.

Solution:

We have found wise to use Machine learning, it means to teach the program detect needed letters and numbers and return correct result. We had dataset for training, which contained images with captchas and correct answers. We have used it for training the model. Then we applied the model to test dataset (only images) and have got the string with answers.