can I know what is the different between waterfall model and object oriented model? Since the book and web dint provide much info about it. Thankyou.
An appropriate comparison would be to iterative development or one of the Agile methodologies.
The waterfall model is the traditional model of the software life cycle. The waterfall model is sequential. The approach for waterfall model is:
Analyze, Design, Build, Test, Maintenance.
Waterfall model concentrates on software development cycle.
The object oriented model focuses on software design and its implementation.
Classes, Objects, Polymorphism, Inheritance, Coupling, Data dependencies.
Related
I am going through the book "Patterns for API Design: Simplifying Integration with Loosely Coupled Message Exchanges" and came across this paragraph. I am having a hard time understanding it
Some software engineering and object-oriented analysis and design
(OOAD) methods balance processing and structural aspects in their
steps, artifacts, and techniques; some put a strong emphasis on either
computing or data. Domain-driven design (DDD) [Evans 2003; Vernon
2013], for instance, is an example of a balanced approach.
Entity-relationship diagrams focus on data structure and relationships
rather than behavior. If a data-centric modeling and API endpoint
identification approach is chosen, there is a risk that many CRUD
(create, read, update, delete) APIs operating on data are exposed,
which can have a negative impact on data quality because every
authorized client may manipulate the provider-side data rather
arbitrarily. CRUD-oriented data abstractions in interfaces introduce
operational and semantic coupling.
There is actually a lot I do not understand in this but In particular I am having difficulty with this part
CRUD-oriented data abstractions in interfaces introduce
operational and semantic coupling.
I know what CRUD is but what does data abstractions mean in this context? How does it relate to the endpoint?
what is operational and semantic coupling?
I am a beginner in machine learning. I want to build a model for finding trending feeds like Instagram.
Please suggest which model is recommended for the same.
I will suggest you to choose these modeling frameworks like Modeling choices, Data freshness trading, and Novelty effect, Experimentation (A/B) Small Effects, Impact, and Scientific Method, Normalization, Iteration Speed — Offline Analysis, Value Modeling, and Parting Thoughts.
Moreover, since you are a beginner, you can get expert guidance for machine learning related questions at Mayazbridge.com. Mayazbridge is the software training institution in kukatpally giving postgraduate courses with career guidance. Hope my answer helps you.
I am trying to learn domain-driven design (DDD), and I think I got the basic idea. But there is something confusing me.
In DDD, are the persistence model and domain model different things? I mean, we design our domain and classes with only domain concerns in mind; that's okay. But after that when we are building our repositories or any other data persistence system, should we create another representation of our model to use in persistence layer?
I was thinking our domain model is used in persistence too, meaning our repositories return our domain objects from queries. But today, I read this post, and I'm a little confused:
Just Stop It! The Domain Model Is Not The Persistence Model
If that's true what would be the advantage of having separate persistence objects from domain objects?
Just think of it this way, the domain model should be dependent upon nothing and have no infrastructure code within it. The domain model should not be serializable or inherit from some ORM objects or even share them. These are all infrastructure concerns and should be defined separate from the domain model.
But, that is if you're looking for going for pure DDD and your project values scalability and performance over speed of initial development. Many times, mixing infrastructure concerns with your "domain model" can help you achieve great strides in speed at the cost of scalability. The point is, you need to ask yourself, "Are the benefits of pure DDD worth the cost in the speed of development?". If your answer is yes, then here is the answer to your question.
Let's start with an example where your application begins with a domain model and it just so happens that the tables in the database match your domain model exactly. Now, your application grows by leaps and bounds and you begin to experience performance issues when querying the database. You have applied a few well thought out indexes, but your tables are growing so rapidly that it looks like you may need to de-normalize your database just to keep up. So, with the help of a dba, you come up with a new database design that will handle your performance needs, but now the tables are vastly different from the way they were before and now chunks of your domain entities are spread across multiple tables rather than it being one table for each entity.
This is just one example, but it demonstrates why your domain model should be separate from your persistence model. In this example, you don't want to break out the classes of your domain model to match the changes you made to the persistence model design and essentially change the meaning of your domain model. Instead, you want to change the mapping between your new persistence model and the domain model.
There are several benefits to keeping these designs separate such as scalability, performance, and reaction time to emergency db changes, but you should weigh them against the cost and speed of initial development. Generally, the projects that will gain the most benefit from this level of separation are large-scale enterprise applications.
UPDATE FOR COMMENTATORS
In the world of software development, there is Nth number of possible solutions. Because of this, there exists an indirect inverse relationship between flexibility and initial speed of development. As a simple example, I could hard code logic into a class or I could write a class that allows for dynamic logic rules to be passed into it. The former option would have a higher speed of development, but at the price of a lower degree of flexibility. The latter option would have a higher degree of flexibility, but at the cost of a lower speed of development. This holds true within every coding language because there is always Nth number of possible solutions.
Many tools are available that help you increase your initial development speed and flexibility. For example, an ORM tool may increase the speed of development for your database access code while also giving you the flexibility to choose whatever specific database implementations the ORM supports. From your perspective, this is a net gain in both time and flexibility minus the cost of the tool (some of which are free) which may or may not be worth it to you based on the cost of development time relative to the value of the business need.
But, for this conversation in coding styles, which is essentially what Domain Driven Design is, you have to account for the time it took to write that tool you're using. If you were to write that ORM tool or even write your database access logic in such a way that it supports all of the implementations that tool gives you, it would take much longer than if you were to just hard-code the specific implementation you plan on using.
In summary, tools can help you to offset your own time to production and price of flexibility, often by distributing the cost of that time to everyone who purchases the tool. But, any code including the code that utilizes a tool, will remain affected by the speed/flexibility relationship. In this way, Domain Driven Design allows for greater flexibility than if you were entangle your business logic, database access, service access, and UI code all together, but at the cost of time to production. Domain Driven Design serves Enterprise level applications better than small applications because Enterprise level applications tend to have a greater cost for the initial development time in relation to business value and because they are more complex, they are also more subject to change requiring greater flexibility at a reduced cost in time.
In DDD, are persistence model and domain model different things?
In DDD you have the domain model and the repository. That's it! If inside the repository you will persist the domain model directly OR if you will convert it to a persistence model before persisting it, it's up to you! It's a matter of design, your design.
The domain doesn't care about how models are saved. It's an implementation detail of the repository and it doesn't matter for the domain. That's the entire purpose of Repositories: encapsulate persistence logic & details inside it.
But as developers we know it's not always possible to build a domain 100% immune from persistence interference, even they being different things. Here in this post I detail some Pros & Cons of having the domain model completely free and isolated from the persistence model.
In DDD, are persistence model and domain model different things?
Yes, but that does not necessarily imply a different set of classes to explicitly represent the persistence model.
If using a relational database for persistence an ORM such as NHibernate can take care of representing the persistence model through mappings to domain classes. In this case there are no explicit persistence model classes. The success of this approach depends on that mapping capabilities of the ORM. NHibernate, for example, can support an intermediate mapping class through component mappings. This allows the use of an explicit persistence model class when the need arises.
If using a document database for persistence, there is usually even less need for a persistence model since the domain model only needs to be serializable in order to be persisted.
Therefore, use an explicit persistence model class when there is a complex mapping that cannot be attained with ORM mappings to the domain model. The difference between the domain model and the persistence model remains regardless of implementation.
I am looking for practical problem (or implementations, applications) examples which are effectively algoritmized using swarm intelligence. I found that multicriteria optimization is one example. Are there any others?
IMHO swarm-intelligence should be added to the tags
Are you looking for toy problems or more for real-world applications?
In the latter category I know variants on swarm intelligence algorithms are used in Hollywood for CGI animations such as large (animated) armies riding the fields of battle.
Related but more towards the toy-problem end of the spectrum you can model large crowds with similar algorithms, and use it for example to simulate disaster-scenarios. AFAIK the Dutch institute TNO has research groups on this topic, though I couldn't find an English link just by googling.
One suggestion for a place to start further investigation would be this PDF book:
http://www.cs.vu.nl/~schut/dbldot/collectivae/sci/sci.pdf
That book also has an appendix (B) with some sample projects you could try and work on.
If you want to get a head start there are several frameworks (scientific use) for multi-agent systems such as swarming intelligence (most of 'em are written with Java I think). Some of them include sample apps too. For example have a look at these:
Repast:
http://repast.sourceforge.net/repast_3/
Swarm.org:
http://swarm.org/
Netlogo:
http://ccl.northwestern.edu/netlogo
Post edited, added more info.
I will take your question like: what kind of real-world problems SI can solve?
There are alot. Swarm intelligence is based on the complex behaviour of swarms, where agents in the swarm coordinate and cooperate by executing very simple rules to generate an emergent complex auto organized behaviour. Also, the agents often make a deliberation process to make efficient decisions, and also, the emergent behaviour of the swarms allows them to find patterns, learn and adapt to their environment. Therefore, real-world applications based on SI are those that often required coordination and cooperation techniques, optimization process, exploratory analysis, dynamical poblems, etc. Some of these are:
Optimization techniques (mathematical functions for example)
Coordination of a swarm of robots (to organize inventory for example)
Routing in communication networks. (This is also dynamical combinatorial optimization)
Data analysis (usually exploratory, like clustering). SI has alot of applications in data mining and machine learning. This allows SI algorithms to find interesting patterns in big sets of data.
Np problems in general
I'm sure there are alot more. You should check the book:
"Swarm Intelligence: from natural to artificial systems". This is the basic book.
Take care.
Every now and then, I hear someone saying things like "functional programming languages are more mathematical". Is it so? If so, why and how? Is, for instance, Scheme more mathematical than Java or C? Or Haskell?
I cannot define precisely what is "mathematical", but I believe you can get the feeling.
Thanks!
There are two common(*) models of computation: the Lambda Calculus (LC) model and the Turing Machine (TM) model.
Lambda Calculus approaches computation by representing it using a mathematical formalism in which results are produced through the composition of functions over a domain of types. LC is also related to Combinatory Logic, which is considered a more generalized approach to the same topic.
The Turing Machine model approaches computation by representing it as the manipulation of symbols stored on idealized storage using a body of basic operations (like addition, mutation, etc).
These different models of computation are the basis for different families of programming languages. Lambda Calculus has given rise to languages like ML, Scheme, and Haskell. The Turing Model has given rise to C, C++, Pascal, and others. As a generalization, most functional programming languages have a theoretical basis in lambda calculus.
Due to the nature of Lambda Calculus, certain proofs are possible about the behavior of systems built on its principles. In fact, provability (ie correctness) is an important concept in LC, and makes possible certain kinds of reasoning and conclusions about LC systems. LC is also related to (and relies on) type theory and category theory.
By contrast, Turing models rely less on type theory and more on structuring computation as a series of state transitions in the underlying model. Turing Machine models of computation are more difficult to make assertions about and do not lend themselves to the same kinds of mathematical proofs and manipulation that LC-based programs do. However, this does not mean that no such analysis is possible - some important aspects of TM models is used when studying virtualization and static analysis of programs.
Because functional programming relies on careful selection of types and transformation between types, FP can be perceived as more "mathematical".
(*) Other models of computation exist as well, but they are less relevant to this discussion.
Pure functional programming languages are examples of a functional calculus and so in theory programs written in a functional language can be reasoned about in a mathematical sense. Ideally you'd like to be able to 'prove' the program is correct.
In practice such reasoning is very hard except in trivial cases, but it's still possible to some degree. You might be able to prove certain properties of the program, for example you might be able to prove that given all numeric inputs to the program, the output is always constrained within a certain range.
In non-functional languages with mutable state and side effects attempts to reason about a program and 'prove' correctness are all but impossible, at the moment at least. With non-functional programs you can think through the program and convince yourself parts of it are correct, and you can run unit tests that test certain inputs, but it's usually not possible to construct rigorous mathematical proofs about the behaviour of the program.
I think one major reason is that pure functional languages have no side effects, i.e. no mutable state, they only map input parameters to result values, which is just what a mathematical function does.
The logic structures of functional programming is heavily based on lambda calculus. While it may not appear to be mathematical based solely on algebraic forms of math, it is written very easily from discrete mathematics.
In comparison to imperative programming, it doesn't prescribe exactly how to do something, but what must be done. This reflects topology.
The mathematical feel of functional programming languages comes from a few different features. The most obvious is the name; "functional", i.e. using functions, which are fundamental to math. The other significant reason is that functional programming involves defining a collection of things that will always be true, which by their interactions achieve the desired computation -- this is similar to how mathematical proofs are done.