I can't understand the difference between logical design and physical design. Can you explain the differences between them like you can list 5 differences?
We usually come up with logical design at high level, using straw-man kind of diagram which you can do in a whiteboard or powerpoint, whereas the physical diagram we use any standard UML diagram based on the need.
The requirements will be input to the logical diagram, for physical diagram - logical diagram will be the input.
The logical design is more conceptual and abstract than the physical design. In the logical design, you look at the logical relationships among the objects. In the physical design, you look at the most effective way of storing and retrieving the objects. Your design should be oriented toward the needs of the end users.
More details: https://docs.oracle.com/cd/A81042_01/DOC/server.816/a76994/logical.htm#:~:text=The%20logical%20design%20is%20more,needs%20of%20the%20end%20users.
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I have been reading papers about the Markov model, suddenly a great extension like TML(Tractable Markov Logic) coming out.
It is a subset of Markov logic, and uses probabilistic class and part hierarchies to control complexity.
This model has both complex logical structure and uncertainty.
It can represent objects, classes, and relations between objects, subject to certain restrictions which ensure that inference in any model built in TML can be queried efficiently.
I am just wondering why such a good idea not widely spreading around the area of application scenarios like activity analysis?
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My understanding is that TML is polynomial in the size of the model, but the size of the model needs to be compiled to a given problem and may become exponentially large. So, at the end, it's still not really tractable.
However, it may be advantageous to use it in the case that the compiled form will be used multiple times, because then the compilation is done only once for multiple queries. Also, once you obtain the compiled form, you know what to expect in terms of run-time.
However, I think the main reason you don't see TML being used more broadly is that it is just an academic idea. There is no robust, general-purpose system based on it. If you try to work on a real problem with it, you will probably find out that it lacks certain practical features. For example, there is no way to represent a normal distribution with it, and lots of problems involve normal distributions. In such cases, one may still use the ideas behind the TML paper but would have to create their own implementation that includes further features needed for the problem at hand. This is a general problem that applies to lots and lots of academic ideas. Only a few become really useful and the basis of practical systems. Most of them exert influence at the level of ideas only.
The AKF scale cube is a qualitative mean to measure the scalability of a system.
This model has been introduced in the book the "Art of scalability". You can find a succinct description here.
I am wondering if there are alternatives to the scale cube, to assess qualitatively the scalability of a system.
(In case this question is off-topic, let me know if there are better suited places for this kind of questions).
In addition to AKF, there is Transactional Auto Scaler (TAS), Scalability Patterns Language Map(SPLM) and Person Achieved Ubiquitous Manufacturing(PAUM)...
This pdf describes the assessment models (Quantitative/Qualitative) of scalability, in different areas such as: Manufacturing Systems, Computer Science, Organizational and Business.
Edit1
If the above models do not measure or at least help measure -I think-, please consider this researche
to measure the scalability which discusses several technics.
Scalability can be measured in various dimensions, such as:
Administrative scalability: The ability for an increasing number of organizations or users to easily share a single distributed system.
Functional scalability: The ability to enhance the system by adding new functionality at minimal effort.
Geographic scalability: The ability to maintain performance, usefulness, or usability regardless of expansion from concentration in a local area to a more distributed geographic pattern.
Load scalability, And so on..
AKF looks like a model not an approach to measure scalability, from the definition:
"The Scale Cube helps teams keep critical dimensions of system scale in mind when solutions are designed and when existing systems are being improved. "
I am currently completing a postgrad degree in Information Systems Management and have been given a thesis topic that relates to Formal Concept Analysis.
The objective is compare open-source software that is able to read data and represent this in a lattice diagram (an example application is that of Concept Explorer). Additionally, the performance of these tools need to be compared with one another, with varying data-set sizes etc.
The main problem I'm experiencing is finding data sets that are compliant with these tools that are big enough to test the limits of each application, as well as figuring out how to accurately measure things such as CPU time taken to draw the lattice diagram and other similar measures. Data for formal contexts generally follow a binary relationship, such as a cross table that shows how attributes and objects can be related.
As such, my question is where would I find such data and how would I be able to manipulate that data to be usable with software like Concept Explorer.
P.S. I am new here, so not sure if this is posted in the right place!
While trying to come up with appropriate features for a supervised learning problem I had the following idea and wondered if it makes sense and if so, how to algorithmically formulate it.
In an image I want to classify two regions, i.e. two "types" of pixels. Say I have some bounded structure, let's take a circle, and I know I can limit my search space to this circle. Within that circle I want to find a segmenting contour, i.e. a contour that separates my pixels into an inner class A and an outer class B.
I want to implement the following model:
I know that pixels close to the bounding circle are more likely to be in the outer class B.
Of course, I can use the distance from the bounding circle as a feature, then the algorithm would learn the average distance of the inner contour from the bounding circle.
But: I wonder if I can exploit my model assumption in a smarter way. One heuristic idea would be to weigh other features by this distance, so to say, if a pixel further away from the bounding circle wants to belong to the outer class B, it has to have strongly convincing other features.
This leads to a general question:
How can one exploit joint information of features, that were prior individually learned by the algorithm?
And to a specific question:
In my outlined setup, does my heuristic idea make sense? At what point of the algorithm should this information be used? What would be recommended literature or what would be buzzwords if I wanted to search for similar ideas in the literature?
This leads to a general question:
How can one exploit joint information of features, that were prior individually learned by the algorithm?
It is not really clear what you are really asking here. What do you mean by "individually learned by the algorithm" and what would be "joiint information"? First of all, problem is too broad, there is no such tring as "generic supervised learning model", each of them works in at least slightly different way, most falling into three classes:
Building a regression model of some kind, to map input data to the output and then agregate results for classification (linear regression, artificial neural networks)
Building geometrical separation of data (like support vector machines, classification-soms' etc.)
Directly (more or less) estimating probability of given classes (like Naive Bayes, classification restricted boltzmann machines etc.)
in each of them, there is somehow encoded "joint information" regarding features - the classification function is their joint information. In some cases it is easy do interpret (linear regression) and in some it is almost impossible (deep boltzmann machines, generally all deep architectures).
And to a specific question:
In my outlined setup, does my heuristic idea make sense? At what point of the algorithm should this information be used? What would be recommended literature or what would be buzzwords if I wanted to search for similar ideas in the literature?
To my best knowledge this concept is quite doubtfull. Many models tends to learn and work better, if your data is uncorrelated, while you are trying to do the opposite - correlate everything with some particular feature. This leads to one main concern - why are you doing this? To force model to use mainly this feature?
If it is so important - maybe a supervised learning is not the good idea, maybe you can directly model your problem by appling set of simple rules based on this particular feature?
If you know the feature is important, but you are aware that in some cases other things matter, and you cannot model them, then your problem will be how much to weight your feature. Should it be just distance*other_feature? Why not sqrt(distance)*feature? What about log(distance)*feature? There are countless possibilities, and seek for the best weighting scheme may be much more costfull, then finding a better machine learning model, which can learn your data from its raw features.
If you only suspect the importance of the feature, the best possible option would be to... do not trust this belief. Numerous studies have shown, that machine learning models are better in selecting features then humans. In fact, this is the whole point of non-linear models.
In literature, problem they you are trying to solve is generally refered as incorporating expert knowledge into the learning process. There are thousands of examples, where there is some kind of knowledge that cannot be directly encoded in data representation, yet too valuable to omit it. You should research terms like "machine learning expert knowledge", and its possible synomyms.
There's a fair amount of work treating the kind of problem you're looking at (which is called segmentation) as an optimisation to be performed on a Markov Random Field, which can be solved by graph theoretic methods like GraphCut. Some examples are the work of Pushmeet Kohli at Microsoft Research (try this paper).
What you describe is, in that framework, a prior on node membership, where p(B) is inversely proportional to the distance from the edge (in addition to any other connectivity constraints you want to impose, there's normally a connectedness one, and there will certainly be a likelihood term for the pixel's intensity). The advantage of doing this is that if you can express everything as a probability model, you don't need to rely on heuristics and you can use standard mechanisms for performing inference.
The downside is you need a fairly strong mathematical background to attempt this; I don't know what the scale of the project you're proposing is, but if you want results quickly or you're lacking the necessary background this is going to be pretty daunting.
Is it the flow diagram, User Interface or what?
In the design phase of the SDLC, making solution is focused via system modeling. Uml modeling is performed which is called the Unified modeling language. UML modeling has the following steps
Class diagram
Use case diagram
Collaboration diagram
Sequence diagram
The UML modeling is basically for defining the problem or we can say it is a tool to solve the problem.
More : http://websolace.net/web-design-solutions/sdlc-maintains-the-quality/
From wikipedia:
In systems, design functions and operations are described in detail, including screen layouts, business rules, process diagrams and other documentation. The output of this stage will describe the new system as a collection of modules or subsystems.
The design stage takes as its initial input the requirements identified in the approved requirements document. For each requirement, a set of one or more design elements will be produced as a result of interviews, workshops, and/or prototype efforts. Design elements describe the desired software features in detail, and generally include functional hierarchy diagrams, screen layout diagrams, tables of business rules, business process diagrams, pseudocode, and a complete entity-relationship diagram with a full data dictionary. These design elements are intended to describe the software in sufficient detail that skilled programmers may develop the software with minimal additional input.
http://en.wikipedia.org/wiki/Systems_Development_Life_Cycle#Design
In SDLC (Software Development Life Cycle), given below things covered in Designing phase:
Partition of requirements into hardware & software system.
Designing system architecture.
Creating UML diagrams (class diagram, Use cases, sequence diagrams
and Activity diagram)