Mabble Rabble: April 2017

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:

futurelearn - linked data

hpi - semantic web technologies

mooc list - semantic-web

cambridge semantics - semantic university

19 April 2017

Sports Feeds

mysportsfeeds
optasports (performgroup)
ODF (OBS.TV)

sports api on programmableweb

18 April 2017

16 April 2017

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

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

11 April 2017

Dueling Neural Networks

Dueling Network for Deep Reinforcement Learning
Dueling Deep Q-Networks

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.

5 April 2017

FIBO

Financial Industry Business Ontology