Applied Design Patterns

Design Patterns have proven to be quite useful in software engineering practice. When used appropriately they have proven to have many benefits and have become an indispensable design approach for architects. A design pattern is essentially a reusable approach towards repeatable problems in context. Not only do they have benefits for architects and software engineers/developers but also other role players in an agile process including: project sponsors, project managers, testers as well as users. There are many design patterns and the field is always changing as new anti-patterns are found to disprove the existing approaches making way for new patterns.  These patterns may also be elaborated for use at object and class scope levels. The Gang of Four have become a fundamental aspect of object-oriented theory and design. However, not every one is at home with utilizing such approaches within their mindset in practice. Having patterns baked in to the language is often seen as a good thing. And, perhaps this is a flaw in languages like Java which formally expect software engineers to have a design pattern style of thinking towards software development and even object-oriented design in particular. Whereas, functional programming languages take a different route and are simpler.  Considering that there are a wide array of design patterns available and most with their relevant domain contexts it only seems plausible to have an intelligent template solution as a refactoring tool/library/plugin. This could be one extension to an intelligent agent model as part of the software engineering development process. The pragmatic agent would need to be able to interpret the code at both a logical but also at a more contextual basis and reason on the basis of understanding where it is appropriate to apply the right design pattern even to identify an anti-pattern. This context of software development automation could be extended to provide other uses within the refactoring process of both functional, service/object-oriented programming, data/object modelling, as well as various others as listed below.

23 Gang Of Four Design Patterns

Behavioral: manage relationships, algorithms, responsibilities between objects

• Chain of Responsibility (Object Scope) 
• Command (Object Scope) 
• Interpreter (Class Scope) 
• Iterator (Object Scope) 
• Mediator (Object Scope) 
• Memento (Object Scope) 
• Observer (Object Scope) 
• State (Object Scope)
• Strategy (Object Scope) 
• Template Method (Class Scope) 
• Visitor (Object Scope) 

Structural: build large object structures from disparate objects

• Composite (Object Scope) 
• Decorator (Object Scope) 
• Facade (Object Scope) 
• Flyweight (Object Scope) 
• Proxy (Object Scope) 
• Adapter (Class/Object Scope) 
• Bridge (Object Scope) 

Creational: construct objects able to be decoupled from implementation

• AbstractFactory (Object Scope) 
• Factory Method (Object Scope) 
• Builder (Object Scope) 
• Prototype (Object Scope) 
• Singleton (Object Scope)

Software Design Pattern
Anti-Patterns
SOA Patterns
Data Science Design Patterns
Big Data WorkLoad Design Patterns
Architectural Patterns
Concurrency Patterns
Interactive Design Patterns
Big Data Architectural Patterns
Microservices Patterns
Microservices Architecture Patterns
Service Design Sheet
Linked Data Design Patterns
Ontology Design Patterns
SourceMaking
Enterprise Architecture Patterns
Enterprise Integration Patterns
Cloud Design Patterns

Enterprise Architecture

Enterprise Architecture is a formidable terrain for large organizations seeped in system complexity and poor business alignments. Hence, various formal frameworks and methods were defined to manage the architecture of such deliverables. Often times the technology architecture mimics the dynamics of a business culture or organizational functions. The below is a list of the four key methodologies that are used for enterprise architecture and a further comparison.

• Zachman Framework (Taxonomy)
• TOGAF (Process)
• Federal Enterprise Architecture (Methodology)
• Gartner Methodology (EA Practice)

Startup Stacks

It is always interesting to see what types of technology stacks are being used by startups especially of the ones that have been successful. Compared to enterprises, startups can often times have nominal legacy code and are open to trying out new approaches with bleeding edge technology. The below link sheds some interesting view of what technology stacks are being used by various startups in the industry and to get an idea of the trends across the different tools and services.

Stackshare

Creative Work Licenses for Software

Original work should always be licensed in some way either for open source community or for full disclosure of protection rights. In a competitive world every one is looking for the shining new piece of artifact that could take a digital community by storm. It seems only plausible that one protect their hard work whether for sharing or otherwise. However, the license terms available are very broad and varied for which one has to be fully mindful and aware of the terms. The below are some helpful links in making an informed decision for the best course of action in the selection of an appropriate license term that best suits an artifact or a project requirements.

choose a license

osfreesoft

Comparison_of_free_and_open-source_software_licenses

producingoss

opensource

software license

Microservices Monitoring

Breaking down a system into more granular services guided by the single responsibility principle does have multiple benefits of bounded context. However, it can also add a degree of complexity that requires more extensive monitoring. With multiple services interaction in a distributed systems context implies multiple log files and a need to aggregate them as well as multiple places for network latency issues to arise. One simple approach is to monitor everything in the entire workflow of the services as well as the system as whole but at same time try to get the bigger picture through an aggregation process. Also, add structure to the logs by utilizing correlation IDs which can then provide a guided trail. The need to be responsive can also be important so real time alerting may also be needed in order to avoid cascaded issues. One can abstract away the service from the system for a monitoring strategy.  The current trend towards monitoring is in a holistic way to get the full picture of the entire system including all its sub-systems as well as all the services interaction within it. A break down of the types of things that can be monitored and examples of tools is given below.

Service-Level Tracking:

• check inbound response times, error rates, and application metrics
• check downstream response health, response times of calls, error rates (Hystrix)
• standardize metrics collection process and pipelines
• standardize on logging formats so aggregation is easier
• check system processes for the OS in order to plan for capacity

System-Level Tracking:

• check host metrics like CPU
• check system logs and aggregate them so it is possible to filter on individual hosts
• standardize on single query option for searching through logs
• standardize on correlation IDs
• standardize on an action plan and alert levels
• unify aggregation (Riemann or Suro)

Logstash and Graphite/Collectd/Statsd are also often used in conjunction for the collection and aggregation of logs. One can also apply the ELK stack. The Java Metrics Library can also be utilized to get insights of code during production. There are other tool options available like Skyline and Oculus for anomaly detection and correlation. 

Open Data and Knowledge

OpenData is all about making data freely available for all without restrictions and mirrors other open source initiatives. It often parallels that of Data.gov and Data.gov.uk. To get involved with OpenKnowledge one can check out Open Knowledge Labs. OpenKnowledge working group areas and data process tools are listed below.

Lobbying Transparency

Open Access

Open Bibliography

Open Definition

Open Design & Hardware

Open Development

Open Economics

Open Education

OpenGLAM

Open Government Data

Open Humanities

Open Linguistics

Open Product Data

Open Science

OpenSpending

Open Sustainability

Open Transport

Personal Data and Privacy

Public Domain

Extracting:

Google Scraper Extension for Chrome

Google Sheets

Scraperwiki

gImageReader + Tesseract

Cleaning:

OpenRefine

Google Sheets

Nonmenklature

Analyzing:

Google Sheets

R

Gelphi

Cytoscape

Presenting:

TillMill

D3

FusionTables

Gelphi

ManyEyes

Cytoscape

Sharing:

DataHub

Github

Google Docs

Further details can be found on School of Data.

Open Data Institute

Computational Linguistics and NLP Conferences

The below link provides the entire calendar of schedule for computational linguistics and natural language processing conferences in play globally for the year as well as an archive of dates.

compling and nlp conferences

ICML 2015

This year the International Conference on Machine Learning took place in Lille, France. It was a fantastic event to bring research from a diverse areas of Machine Learning in a collaborative setting. The conference went down really well. There was an immense amount of research shared within the community.  Also, a noticeable increase in number of people that attended the conference this year. The schedule was broken down into conferences, workshops, and tutorials. Even an open question and discussion session was available after each session. The banquet was a joyful experience. However, both the banquet and the local Lille food experience was much to be desired. Cheese was on display, in all forms, and showing itself in every french menu. For vegetarians, Lille offers cheese, french fries, and salad. Some of the most popular areas of research covered included: Deep Learning, Topic Modelling, Structured Prediction, Networks and Graphs, Natural Language Processing, Reinforcement Learning, and Transfer Learning. Deep Learning, Reinforcement Learning, and Word2Vec were the most popular researched topics in attendance. Many of the presented papers can be found also on Arxiv. The conference also showed how far Machine Learning has come as well as the level of popularity it has garnered over the years. Machine Learning is proving to be an invaluable area in a multitude of domains which is having profound effects for business and society as a whole.  But, one thing was reverberated throughout the conference that even now there is still a lot to be discovered before Artificial Intelligence can truly match the abilities of a human being.