20 April 2017

Arachnys Sanctions Checker

Arachnys Sanctions Checker

OpenNames

OpenNames

Swift Sanctions List Distribution

Swift Sanctions List Distribution

HM Consolidated List of Targets

Consolidated List of Targets

Consolidated Sanctions List

Consolidated Sanctions List

EU Consolidated List

consolidated list of persons groups and entities subject to eu financial sanctions

Consolidated Screening List

Consolidated Screening List

Transparency International

Transparency International's Corruption Perceptions Index 2014

Linked Data Patterns

Linked Data Patterns

Semantic Web Meetup Course

semantic web london
metadataconsulting

  • Introduction to Semantic Web standards and Linked data technologies 
  • Resource Description Framework 
  • Graph-based data model representation and core concepts 
  • Terse RDF Triple Language 
  • Advanced RDF features 
  • Best practices on publishing RDF data 
  • RDF Schema (RDFS) 
  • Discussion of the added value of a schema driven by examples 
  • Syntax of the core features: classes, properties and their characteristics 
  • Relationships between RDFS vocabulary elements 
  • Computing answers to typical queries over RDFS datasets 
  • Using Protege for modeling and querying RDFS datasets 
  • Limitations of RDFS 
  • Querying Semantic Web with SPARQL 
  • Core concepts 
  • Basic graph patterns 
  • Querying datasets with the SPARQL engine StarDog 
  • Filters and SPARQL expressions 
  • Property path expressions 
  • Complex graph patterns with advanced features such as optional parts, aggregation and ordering Other query types 
  • Updating with SPARQL 
  • OWL Web Ontology Language  
  • Core concepts and differences to RDFS 
  • Overview of OWL modeling constructs
  • Modeling and assessing the benefits of alternative models in a particular application context Substitutability of modeling constructs 
  • Discussion of the trade-off between the expressivity of modeling languages and the computational efficiency of querying 
  • OWL profiles 
  • Limitations of the expressive power of OWL 
  • Applications of Semantic Technologies in Practice

Alternatives:

16 April 2017

MXNet

MXNet

Scala vs Go Concurrency

Scala:
  • Immutable and persistent data structures
  • First-Class Functions and Closures
  • Concurrency and Remoting with Actor model
  • Software Transactional Memory


Go:
  • Expressive lightweight machine code driven
  • Go-routines and unix pipe-like channels
  • Isolated mutability abstractions for concurrency
  • High-speed compilation

12 April 2017

Mesos Frameworks

Marathon: launch and manage long-running applications
Chronos: cluster scheduler
Aurora: manage long-running services and cron jobs
Singularity: PaaS for running services
Marathoner: service discovery for marathon
Consul: orchestration and service discovery
HAProxy: load balancing
Bamboo: automatically configure HAProxies
Fenzo: task scheduler
PaaSTA: PaaS for running services

10 April 2017

NoSQL via Cap Theorem

AP
Amazon Dynamo
Voldemort
Cassandra
CouchDB
Amazon SimpleDB
Riak

CP
Google BigTable
HBase
MongoDB
Redis
MemcacheDB

CA
MySQL
Postgres

7 April 2017

Optimization for Taxi Drivers

Building an accurate recommendation for taxi drivers to optimize on their customer earnings with view of planned route journeys is a hard problem. The recommendations need to take into account customers as well as the driver profiles. How big should the circumference be to cover a driver's route recommendation journeys? What will be a good enough benchmark for recommendations?

Take for example the imaginary case between two drivers and their customers. Suppose there exists Driver1 that is located in Piccadilly Circus, a Driver2 located in Oxford Circus, and potential customers are situated equidistant from both drivers on Regent Street, who have a cumulative score of 30% likelihood that one of the customers will want to use a taxi cab. But, one does not know which customer. How will the recommendation work for both drivers? Should one take into account the driver profiles as well as the behavior, intents, and propensity of the customers? However, if one takes into account customer demographics and the opportunity cost of the drivers in satisficing on game theory then how will their recommendations provide an optimization threshold that is dynamically balanced and suited for both drivers at any given time, supposing the drivers are continuously driving and the customers are unpredictable in their direction of movement. Is looking at individually for each driver optimization within their immediate clustering radius sufficient to track for hot spots for nearest customers? What if the geolocation is also a densely populated hot spot for other taxi drivers as in more vs less driver competition in the area.

The issues in recommendations are further compounded by uncertainties of the road network and the variability of drivers as well as the number of geolocated customers who are potentially seeking a taxi cab ride at any given time and at any given direction of road traffic. Other influencing factors could include: public/private events, seasonal variations, weather, security, roadworks, traffic congestion, public transport, density of customers, shopping arcades, poor vs rich neighborhoods, other rural and urban dynamics.

One solution to this problem could incorporate a complex network representation of road network which incorporates a semantically defined probabilistic reasoning similar in respect to a Bayesian or a Markov Chain Monte Carlo approach to generalize over all the drivers. At any given point of recommendation one can then query for drivers and their probabilistic customers within the neighboring geolocations. Additionally, an aggregation step could include a combination of regression and clustering for individual weighted driver recommendations.