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Turning Data into Information with Geo-Ontologies

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and what do they share a intelligence software were for lack of a
better term doing with things like consolidating
disparate data sources and by giving them the entities ability to map out of um specifically we worked
with our Emily international not province but also work in the oil and gas phase as well as advertising but primarily of most of what we've done is international development space specifically events scary
creatures like this and spreading malaria
across large swaths this is is the
difficult space to work working as you can imagine what of the customer needs are quite difficult to achieve and we start working on some was 9 years ago where a and the state of technology is different in but we was
this is not easy to achieve that goal in to do things like to prevent malaria and across the world but some
common some common problems we found across process thousands of years and then pretty common themes and share a lot of you things like being consolidated consolidate disparate data sources entities having limited resources specifically in the stock GIS technology but a lack of the data this procedure tested especially coming from the trees and need to consolidate and mapping it's a pretty common things venture a lot of you experience these the country at all is
the and a specifically the need to turn this in the summer of so how to use the
term frame 1st there is a set of
in fast tools in general with our the possum work by these groups there's no way a small company like us to do what we've done so far so thank you but but additionally we need another way to approach data something in that it was practically non-existent land use of we chose the technique of modeling data as ontologies so every time that
area is chairman of the program was in that I that's so I'm here to try to
dispel some of those concerns by giving you a ontology now that variable that this topic the of in but that's where a lot of for it to into a need to
explain those 2 primary happens in this ontology data and user you have patient ontologies in general is to find of so to start with
ontologies where and I told you the cial programming that allows us to make you like inferences about so you have basic ontology something like just a person or is there is a lot of Justin has brain again this has the and you ontology is really similar to that what of speech so Colorado the 4 columns of the United States if use the a very human life references to in the in order for us to use in the stack the entire about half of the other and come to central concepts 1 is universal others you have sufficiently to point you universal is essentially a collection of you have this kind of political and you were in countries states provinces districts several kinds of during the individual features in that collection in the South Korea the country in the country's universe the
time was formerly but in this diagram can see during the summer that represent individual features within the universal set the Colorado State they found that state universal and the universals have relationship among between so the 1st of the state of the country's accountant and when it is a simple tree which you might say that so far in the state within a state universal in travel down in response to a man in the online relationships between speech so
was 1st of all we do this right the processing for it is vital context was specifically provides a well-defined spatial and non-spatial relationship between and notice that the the on geometry so this means that we can invoke into our system does have to mention here so the company itself while the and still work speech the so this would be talking about here is run and use that money and it uses that and that that will also consider the ontology and so it's really more easily than going to adapt the visualization of the users about all this is on top of it and working on magnitude of this presentation but I really quick screenshot you of of the of the that you so what is it
like is is the 1st quantity that it provides a reference level of spatial data inside of the 2 that this will hire is most which I use it in a a little bit about the nature of the data coming from
the case of the domain of knowledge so you're working with malaria you're using the like he's itself along with the malaria not necessarily matter to us of spatial but you need to have relationships to ontology which gives us a lot of power to the kinds of trees or make and so on or any other ideas and what not itself and as we all know accelerator and all the other we mentioned in this job but what about so the topic is and this is
the slide you try to explain how you cannot get this simple structure the years on the left you have data coming in the images on the eye and so it's part the system and of the topic was was was the standard records of data that matters against you and using a location of so again is the geometry in this case we're working semantics every customer that work with work with semantics labels the names of countries you can't always do you have geometry so you map that the user records against a duality which automatically give users records reference to the spatial processing in the system itself and with this lower here the lower branch of the system maps a synonym to this mechanism mechanisms in that type of thing and semantic differences between locations to a single you and so for candidate in data in the system that we know that there's a known type the system you register that type of thing and every single record coming in future that I will not see it also quite a lot of the and finally we have used the internet against the the structure of the GUI is I you have to get all all power of working with the University but that thank you the so wise is
valuable I mentioned some of the reasons why but a big reason why is it allows us to map data integer generic way what I mean
by generic I mean my data your
data everyone's data doesn't matter what data you have does matter what formats in we can pipe endorses and map against ontologies as long as you have something that indicates location it could be a geometry it could be at the at the field indicating some text location and so on has as something but the doors pretty wide open that's so it opens it it gives a lot of flexibility in the in terms of the types of data and users we can interact with original help so issues like new geometries
like if had multiple times not a problem at all of course if we have geometries it only adds storability that build apps and help help and deliver better solutions but it isn't a requirement to get the system running and provide visualization and analysis so how does this work in a web
application is kind of a big leap but
out but this diagram demonstrates something you should be familiar with by now but very had user data and you entities we've maps this user data against some known entities and because the universal hierarchy has some awareness I we can aggregate these you entities up to the parent universal entities so will know that by summing all the records the join with but these 2 due entities the Colorado has sold 5 digits and we can aggravate up there the i universal stacking and further to the United States to see this 5 widgets were sold in the United States and the beauty of this is it works with all types of data so they have to be sales could be by counting bodies the desert the is a reiteration of what is said following so what about geometries keep
saying how we don't need a however the incredibly useful geometries are still used to visualize you entities so everything you see in a map will be a geometry stored on the duality if the janitors not have geometric data can be visualized 1 at the students if it's an ontology record of user data it's can still be mapped against the that you energy the hazard you with sites can be confusing combining afterwards that's confusing so it also allows us to visualize user data at the lowest levels of points that we can excel file was lat-long coordinates we still allow users to visualize and have to aggregate the data of the universe hierarchy to let's see on because the geometry geometries algorithmically enhanced data audits and QA QC and find data coming in the system 1st just like I always geometries incredibly useful just we had and find a solution for mapping visualizing data doesn't require so of them point
and then again that important point is that these young trees so we currently deployed this technology
this by checking the countries were expected to be and 14 within the next 6 months and we have a lot of other uh the expansion of opportunities coming up since we we fully expect to be on possibly quadrupling the amount of countries we with this technology to in the next couple years which is really great for a very small companies 7 4
but so like I said I don't have the ability to have my computer up to this at but to this computer was but luckily I the little desktop screencast of the most basic use and your dashboard so we can get this and here the comedy on
time from the screen at the
I but so
so I really wish I could click through my computer and love to show anyone who is interested in this later be happy sit down at the in what he listen Due dashboard is is much more feature-rich and you're about to see here and so this is the basic dashboard as you can see very familiar to all of us we have layers on the left and uh some data item a list of data on the right but that so this right here is a simple dataset you can mention this is user data again we can pipe any any user data into the systemic more than more than 1 here we only have 1 case delivery summary actually stands for is our Cambodian about that terminology for sales salesman and we have a bunch of actually it's on the data on the right M. so they play real quick
here and simply opening of the form Ted create a letter so this is a re-enable budget dynamic mapping of very square is much like a lot of uh the other I posted mapping the solution laughter and
what so maps and data but then if you wanna work
with the data and analyze a little further you can change a aggregation methods so before we we aggregated by our province but I
just changed the aggregation level to district so here's an example of us navigating of the universal tree to dynamically map data from the base of an ontology structure what there we have
a bunch of sales data representatives point size by the number of sales and and mapped against a generic in the ontology structure so 1 other interesting peace to this
is the ability to filter data so they get is the real quick on that example but on the right you can manipulate the data through simple little widgets I cure working with a number of fields so it's a simple agree that parties data values greater or less than equal to whatever on but you can also query on ontology types and the date time all that stuff to work with the data it's really nice your training sequence and 1 final
thing we have is a bunch of and
management data management tools to work with but the ontology data in the
back and secure navigating the universal tree users can move these universals around the system to redefine the universal
hierarchy they can also visualize all the geo entities in the system period which juries for Cambodians and Zambia and a bunch of us identify problems that are identified through the ontology structure so again there's no spatial querying oppose just behind the scene to figure out what these problems are as all derived from ontology information and here were our during going to confirm a new location were fixed a problem in the geo entities to through a simple additive nothing I believe this is the and of the smoothing following the does anyone have any
questions the that
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Titel Turning Data into Information with Geo-Ontologies
Serientitel FOSS4G Seoul 2015
Autor Lewis, Justin
Lizenz CC-Namensnennung - keine kommerzielle Nutzung - Weitergabe unter gleichen Bedingungen 3.0 Deutschland:
Sie dürfen das Werk bzw. den Inhalt zu jedem legalen und nicht-kommerziellen Zweck nutzen, verändern und in unveränderter oder veränderter Form vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen und das Werk bzw. diesen Inhalt auch in veränderter Form nur unter den Bedingungen dieser Lizenz weitergeben.
DOI 10.5446/32072
Herausgeber FOSS4G
Erscheinungsjahr 2015
Sprache Englisch
Produzent FOSS4G KOREA
Produktionsjahr 2015
Produktionsort Seoul, South Korea

Inhaltliche Metadaten

Fachgebiet Informatik
Abstract DESCRIPTION: Organizations of all sizes face issues harmonizing data between disparate sources in a way that is both efficient and useful for analysis and visualization. Geo-Ontologies offer an approach to data management that enables flexibility for interacting with data in a generic context even if the data is lacking geometries or contains problematic text errors. RunwaySDK is an open source ontology engine which empowers robust web visualizations to serve the analytical needs of organizations both large and small. Built on open source tools and driven by real world needs, GeoDashboard (also Open Source) exposes the flexibility gained from RunwaySDK by empowering users with robust features for managing and visualizing their data from a web-browser. This talk will focus on how GeoDashboard's use of Geo-Ontologies enables dynamic mapping of almost any dataset in meaningful ways to fight disease and sanitation issues in developing countries. ABSTRACT: Ontologies in software development are a way to apply human like inferences to data, such as a bee is an insect. Geo-Ontologies focus on the geographic relationships of ontologies, such as Seoul is within Korea. Ontologies offer a valuable approach to data management because it allows for building a complex network of structured relationships. These well defined relationships can also be used to analyze and map data regardless of whether the data points include geometries. Using this approach to software development coupled with an open source business model has enabled TerraFrame to develop the mature ontology based data engine RunwaySDK and the powerful map based visualization layer GeoDashboard. RunwaySDK has been used in conjunction with an application tier to fight vector borne disease in multiple countries. GeoDashboard is a newer open source application built with PostGIS, GeoServer, Leaflet.js, and RunwaySDK which enables users to gain control over both data management and visualization all from a web-browser. The goal of GeoDashboard is to give organizations of all sizes the means to solve and share difficult problems through easy and accessible tools. This talk will introduce the basics of RunwaySDK's Geo-Ontology model and how it is being used in GeoDashboard to allow users to: *Dynamically map layers aggregated against political boundaries *Dynamically map layers with different geometric and cartographic representations *Dynamically filter data across related layers in a map *Dynamically query ontology data that layers are mapped against *Manage data relationship structures through ontology web widgets *Manage geographic data through web widgets *Expose data quality issues through web widgets

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