I am trying to use semantic technologies in IOT. From the last two months I am doing literature survey and during this time I came to know some of the tools required like (protege, Apache Jena). Now along with reading papers I want to play with semantic techniques like annotation, linking data etc so that I can get the better understanding of the concepts involved. For the same I have put the roadmap as:
Collect data manually (using sensors) or use some data set already on the web.
Annotate the dataset and possibly use ontology (not sure)
Apply open linking data principles
I am not sure whether this road map is correct or not. I am asking for suggestions in following points
Is this roadmap correct?
How should I approach for steps 2 and 3. In other words which tools should I use for these steps?
Hope you guys can help me in finding a proper way for handling this issue. Thanks
Semantics and IoT (or semantic sensor web [1]) is a hot topic. Congratulations that you choose a interesting and worth pursuing research topic.
In my opinion, your three steps approach looks good. I would recommend you to do a quick prototype so you can learn the possible challenges early.
In addition to the implementation technologies (Portege, etc.), there are some important works might be useful for you:
Open Geospatial Consortium (OGC) Sensor Web Enablement (SWE). [2] It is an important work for sharing and exchanging sensor observation data. Many large organizations (NOAA, NASA, NRCan, AAFC, ESA, etc.) have adopted this standard. This standard has defined a conceptual data model/ontology (O&M, ISO 19156). Note: this is a very comprehensive standard, hence it's very BIG and can be time consuming to read. I recommend to read #2 mentioned below.
OGC SensorThings API (http://ogc-iot.github.io/ogc-iot-api/), a IoT cloud API standard based on the OGC SWE. This might be most relevant to you. It is a light-weight protocol of the SWE family, and designed specifically for IoT. Some early research work has been done to use JSON-LD to annotate SensorThings.
W3C Spatial Data on Web (http://www.w3.org/2015/spatial/wiki/Main_Page). It is an on-going joint work between W3C and OGC. Part of the goal is to mature SSN (Semantic Sensor Network) ontology. Once it's ready, the new SSN can be used to annotate SensorThings API for example. A work worth to monitor.
[1] Sheth, Amit, Cory Henson, and Satya S. Sahoo. "Semantic sensor web." Internet Computing, IEEE 12.4 (2008): 78-83.
[2] Bröring, Arne, et al. "New generation sensor web enablement." Sensors 11.3 (2011): 2652-2699.
Related
I am a very beginner in software and I am asking or a direction to proceed for research technologies to build my app. I am having just an idea for the app. I am trying to build something like zomato but different services. The idea of location based system is similar. I searched online and came to know about GIS systems. But while researching further, it seems I've to create a map all together. This feels redundant to build as we have api of google maps.
But can i use this api to build a system "ON" it????
Any tutorials or some direction in this direction would be helpful.
Also what is difference between GIS and gps based apps.
As you see, I am not very clear in the fundamentals of the GIS and GPS based apps
Thanks for the help
Regarding Android, you have almost all you need by combining the platform API and the comprehensive Google Maps Android API. Regarding the later, it's actually a matter of opting by convenience and possibly paying a licence fee to Google, versus developing your own solutions of aggregating free or cheaper services from elsewhere.
Most problems solved by apps are not the same problems solved by classical GIS software, since the former are more consumer-oriented (using public transportation, navigating a route, planning a trip, finding a nearby restaurant), and the later are more specialist-oriented, typically solving larger-scale and more technical issues (detecting regions with flood risk, monitoring deforestation, calculating volumes of terrain to be bulldozed, etc.)
You should not, IMO, be discouraged by the seemingly hard technical concepts of geography and map making. Your best bet is to have a clear vision of what actual problems you app should be solving, and study the geography topics gradually, as the need arises.
A bit of consideration on your question about GIS:
If it were created today, the GIS acronym would mean any software dealing with geographic data, be it a mobile app or a workstation software suite destined to specialized professional use.
But when it was created, the term meant almost exclusively the later sense, and so it has a lot of tradition and cultural legacy to it - which is of couse not always a good thing. Specifically (at least in my experience), it seems to me the jargon and concepts used by the classic GIS community are a bit impenetrable to the newcomer, specially if she comes from the software-development field instead of the geo-sciences field.
But geographic information availability has gone from scarcity to overwhelming abundance, and so have its enabling technologies: GPS satellites, mobile computing and mobile connectivity.
Is POWDER (http://www.w3.org/TR/powder-dr/) meant to be used for Trust/ Proof layers of the Semantic Web? Are there any other w3c standards made for the same layers?
Thank you in advance
Well, the upper layers are pretty much still science fiction, and no one knows whether they will work out exactly in the way suggested by the usual layer cake diagrams.
There are some standards that might be useful as building blocks for supporting these higher layers. POWDER is one of them. Named Graphs (as standardised in SPARQL, and currently under consideration as a native feature of RDF 1.1) is another one. The W3C Provenance Ontology is yet another one.
Then there's plenty of proposals and research ideas that are not yet ready for standardisation.
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.
I'm currently using a Processing Kinect library which supplies a depth map. I was wondering how I could take that and use it to create a 2D skeleton, if possible. Not looking for any code here, just a general process I could use to achieve those results.
Also, given that we've seen this in several of the Kinect games so far, would it be difficult to have multiple skeletons running at once?
Disclaimer: the reason why you still didn't get an answer for this question is probably because that's a current research problem. So I can't give you a direct answer but will try to help with some information and useful resources for this topic.
There are mainly 2 different approaches to create a skeleton from a depth map. The first one is to use machine learning, the second is purely algorithmic.
For the machine learning one, you'd need many samples of people doing a predetermined move, and use those samples to train your favorite learning algorithm. That's the approach that was taken and implemented by Microsoft in the XBox (source), it works really well BUT you need millions of samples to make it reliable... quite a drawback.
The "algorithmic" approach (understand without using a training set) can be done in many different ways and is a research problem. It's often based on modeling the possible body postures and trying to match that with the depth image received. That's the approach that was chosen by PrimeSense (the guys behind the kinect depth camera technology) for their skeleton tracking tool NITE.
The OpenKinect community maintains a wiki where they list some interesting research material about this topic. You might also be interested in this thread on the OpenNI mailing list.
If you're looking for an implementation of a skeleton tracking tool, PrimeSense released NITE (closed source), the one they made: it's part of the OpenNI framework. That's what's used in most of the videos you might have seen that involve skeleton tracking. I think it's able to handle up to 2 skeletons at the same time, but that requires confirmation.
The best solution is to use FAAST (http://projects.ict.usc.edu/mxr/faast/) which requires OpenNI. I have struggled to get OpenNI to work on my computer. I have not seen an approach yet using Code Laboratories' CL NUI.
An algorithmic approach is http://code.google.com/p/skeletonization/ but you may have a problem because your depthmap only represents surfaces and no closed objects.
I am looking for a project idea in distributed processing on Unix based systems. I wish to use only the C programming language. I have to finish the project in 4 months and it's a part of my course work. Can someone help me with an idea?
Cryptography problems
Distributed Ray Tracer
Chess AI (really, AI for any game)
Large Prime Number Search
Web crawler or other search mechanism
Generic Problem Solver (push out problem definition on the fly, followed by problem data).
Note on the last one:
An example would be if you have a gaming website with lots of board games that you were coming out with all the time. You don't want to have to install new clients on all your servers every time you write a new AI for a board game, so you have a program which you can send new AIs to and then after that you can just send the game data and the pushed AI will be used to solve the problem. This is best used for problems which can be broken into smaller chunks.
It is hard to answer without knowing anything about performance, the scale of the project, what you are trying to accomplish, etc. For example, is it one task or multiple tasks? Is the project just totally open?
4 months is pretty short, but maybe some kind of physics problem or math problem. Sorting or some kind of database work might be dull but beneficial.
Check out mapreduce for ideas! I was really motivated by this work, personally.
We used distributed processing here at work, but it's such a broad field..
Yeah.
Why not write a distributed compiler. You may then present an interface for people to compile things on the fly, and it will be passed to your distribute compilenet. Java is probably well-suited, and you'll get to do fun things, like be very mindful of security and so on.
The BOINC project is always looking for help and is very interesting:
http://boinc.berkeley.edu/
If you want to leave your mark and change the way we search the web,
look into B-Trees.
B-Trees and offspring/variants are the working horse of the internet.
Google uses them extensively to index the web.
Database indexes/indices are B-Tree offspring/variants.
Every LAMP system uses a database and indexes/indices.
Also, they are used extensively in distributed VLDB (Very Large DataBases)
Perhaps you can improve existing distributed databases (Cassandra and HBase)
These are lofty goals, but for me, this would leave a lasting mark
in the way Web data is processed, indexed and stored.
Write a distributed, fault tolerant, redundant network B+Tree or B*Tree.
Read Drozdek's book Data Structures and Algorithms in C++.
It's a good survey of B-Trees.
Read about skip trees
http://www.cs.huji.ac.il/~ittaia/papers/AAY-OPODIS05.pdf
Read about Efficient B-tree Based Indexing for Cloud Data Processing
http://www.comp.nus.edu.sg/~ooibc/vldb10-cgindex.pdf
Google search "Network B+Tree"
https://www.google.com/search?rlz=1C1CHKZ_enUS431US431&sourceid=chrome&ie=UTF-8&q=Network+B%2BTree