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Big Data Europe - The collaborative creation of an open software platform for researchers addressing Europe's societal challenges

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check and the killer much for the invitation
really wanted to be here the sooner the you even more frequently and I'm talking to today about big data your project which is such projects of land and coordinating but some of you have a number of partners involved
and the Big Data as big public uh hyper topic everybody talks
about big data In many ways it has a negative connotation there so if you think
for example in marketing and big data is used to sell us more stuff which we might not need to analyze customers so the glass customer and is also term in that context and another area where the data is
used probably the biggest state has a surveillance intelligence agencies uh I myself talked Oracle and they confirm that this is 1 of the biggest customers for Denver to develop a lot of advanced Big Data and analytics technologies for also IBM for example 40 and intelligence agencies and of course this is something which we have
to look at very critically and also defend the civil rights of Verity's boundaries are basically tested and the number of observations against citizens yeah but there are and and a large number of of course also all domains in areas that big data
can be really helpful and useful to address societal problems and in Horizon 2020 Darragh 7 as societal challenges and so important topics which arise in 2020 addresses and focuses on was from investments and alignment of the research programs are so the 1st societal challenges health immigrant uh demographic change and well-being but the 2nd 1 food security agriculture forestry marine and very team um bioeconomy unsolved then secure clean and efficient energy on another 1 smart green integrated transport a climate action environment resource-efficiency raw materials and then there is a topic of which comprise a lot of of areas especially from the social
sciences inclusive innovative reflective societies and finally security of protecting the freedom and in Europe so these are
the societal challenges arise 2020 and big communities of researchers of companies of organizations doing research and innovations in these areas and of course they all need to use all sold Big Data technologies nowadays or Data technologies and that was a and 1 of the reasons why this project was a really started and then we looked at in
the classical 3 redefinition of big data we have volume variety velocity as these
dimensions of the data from so volume and refers to a terabyte petabyte of of the time a velocity that we really have the changing data which vary but vastly changes so real-time updates have for example or near real-time data
and that's 1 dimension which we think is a bit neglected as the variety dimension and that was also confirmed the actually performed of many
workshops mistake orders from each of these communities so I we invited to a
lot of people and so on and thematic workshops in each of these areas and ask them what are you big data problems and try to identify of what are the challenges there would be learned is often they don't have the
data and everybody talks about big data but in most cases it's not big in many cases actually even fits on the USB stick and that the challenges the variety of the data so they have heterogeneous data from different sources of and dealing integrating this data
and then once integrated then of course it becomes bigger and bigger and you can integrate more about that the biggest challenge which we
observed to sexually variety and and that's something which the Big Data technology community but neglected the but from my point of view the focused on lots of these components had to walk to around and in graph databases of the processing analytics
frameworks which focus a lot on speeds on the speed of processing but for data dealing with the heterogenity in variety of the data and there was not so
much and support of the sorry looks like some fonts here and that's scrambled to on on so
the big data Europe project is a project that addressing the societal challenges and talking to the communities it's a support action on the 1 hand we of market these communities try to and then if I what are the requirements identify what RDD important issues in the big data era on the other hand we want to also
lauded barrier for using Big Data technologies and every developer platform at technology a software platform which can be used and then by the stakeholders in
these different communities so that they don't have to reinvent the wheel all the time and the project comprises partners from from many of the European countries and so some of these are technical partners like found for for example and like in phi from from let's like of Free University of Amsterdam and so on but the answers are Democritus from Greece uh they also have a representative from the stakeholder communities like the Food and Agriculture Organization some of the
united nations of we have sets and in the security areas of each of the societal challenges we have 1 player which most of the area pretty well there and has a lot of contacts
but also if you look at the data value chains in these different areas so the number of different and the processing
steps from extraction of data or correlation or quality assurance and linking of data integration publication visualization analysis these all different aspects of of processing data and and existing technologies and Big Data aspects they often have to slop ineffective prob proprietary and that's why not so easily usable also especially by the research communities and that's what of the aim of this
project was 4 to provide on technologies for each of these different steps of which can be used and a number of very open man of course many of the big data components are freely available
mostly and I also released under Apache Licence or hosted by the Apache Foundation but bundling them and uh creating these distributions of of of components that this is a big business
model and there are companies like Hortonworks or cloudy of our format are who do big business these all Americans so I'm most of their of many European companies but so in Big Data Europe even to create uh such a generic technology platform for Big Data Value Chain our deployment and so if you
look at the space of big data applications so you see that it's a zoo or and as such a and the nightmare actually 2 to look into this area because you have so many different but technologies components and to figure out what you need for a specific use case encopresis and then uh integrating usually also not just need 1 component but you need to many of them maybe 5 or 7 and combining
them in a meaningful way and uh that requires significant resources and makes it of the
terribly time-consuming and complicated so components on the area of infrastructure databases of different data at paradigms of SQL as uh graph databases but we have analytics so those technologies there are machine learning technologies cognitive technologies on API for data sources of and of course also applications on top of that so on a vast you off of products and technologies in that area
and so we want to help some organizations building
applications on top of the data and technologies uh using open source components and many on even after commercial platforms as I mentioned each the use of open source components and bundle them and and the big data your platform basis on Dockery zation of these components so docker is a paradigm which gained think a really big popularity of flexible lightweight virtualization Our technique but in the last 3
4 years and many of 2 commercial platforms so they started earlier so they have a more in a proprietary way off of managing this component integration and that's an enumeration of the
big data you're platform then so we facilitate the integration and deployment so that you can all the the box basically use this platform and and we also add some additions to be not only core rates of existing components of but we add the support layer of is an integrated user
interface which integrates your eyes of the individual components so that you can and administrator of components in a one-stop shop and and uh another uh innovation is semantification layer rare on the hope of semantifying data using working with semantic data because that's a solution of this variety heterogenity problem and that you'll and
describe your data semantically that to map it to vocabularies for example and and then you can run algorithms a bit on a higher abstraction level and you don't need to hardwire um Grudin so much and we hope that this will and improved the speed and make
it easier to to to work with this data so there was a really is actually last week 1 of the big data era platform of area organize also that in our so you can know
and downloads of the platform and you should uh in applications and use a bit a general architecture may be on the
next slide and have at a bit more larger and detailed of we have of course a hardware
layer so you can deploy does Big Data platform either on premise infrastructure in your own data center or in the cloud you can even run it on your notebook if you're just develop something and that's quite quite also handy useful and then there's a resource management layer features to stop a swarm our infrastructure and the data layer review have different uh technologies still manage and to store data on the semantic layer of which cement defies or or provide semantic access to this data then different processing frameworks like sparkling Kafka of which have different some of the characteristics in terms of performance and scalability of 4 4 different tasks and then you have uh applications of on top of redevelopment in absence of lower and and pilots for each of the societal challenge we have 7 of Jesus and challenges
and for each 1 we have 1 of application demonstration and will show you 1 and a bit more detail in a minute and then support layer
Ferrero where these components basically order and the difference of my instances science is initiated for every have this user interfaces and monitoring and of the and of 2 infrastructure basically 1 very
important aspect is as I said this um semantification layer and every follow an approach usually in big data applications have this they colleague approach many people say data swarm for because of basically the idea is you dump your data onto and HDFS as Hadoop file system but you don't care about integrating the classical approach business intelligence approaches you have to make a lot of effort into ETL Extract Transform Load so you bring your data into 1 coherent schema and then you can run queries on the schema and of the
approach and now in this big data area as more and storing the data in the original format speed C is the example for example also and maybe S. Q. L. or RDF 1 Jason is a popular format and ends up stored as on on on such a scalable data storage infrastructure and then and excessive debate on this infrastructure HDFS so this you should be used as a basis as a file system which can easily scale to a larger clusters
and we are now working on the ends of 1st version of this is already available of adding and more semantics so that's of for the different data formats like databases exam uh or graph databases you then have mappings and and you use basically and vocabularies and some as of semantic correctly and knowledge representation techniques to interpret these data sources so you can then run a courier spot query for example which is decomposed and into different parts and those parts are executed against of the different nature of the data at varying mechanisms of like for XML for example it's
XPath or 4 days and you have these Jason path or graph database query languages for relational database as well of course and and that allows them to excess of different types of data on the homogeneous integrated way so we call the semantic data late and or implementation of that is called Ontario
as 1 of the largest lakes and in the world and the compare a dictator your
platform with an alternatives our dimension kind diffuse actually dominated by this big American players which gained guts of hundreds of millions of of venture capital like Hortonworks cloudy around that are and then there's a patchy protocol that that you pick top and and some the big data Europe with a very small investment of the European Union tried to a complete and he's the little bit in that area of course it's not only
a competition he also benefits of big talk for example if open source and is Mbrola project for all these open source Big Data components which are available have made available by the Apache Foundation so he of course of built on top of that and use the top which
by the way is also used by the other commercial distributions and we also based on a dare to file system for example in terms of uh already mentioned that of virtualization you Stocker which is the more likely it approach or the other of the distributions have native mechanisms for debt which as the result that that's only works if the notes while we can basically you can deploy allowed to be getting your platform on our infrastructure which ones some of which can run blocker which includes also Windows and Mac and then there
are some other properties in particular the past but like these use commercial platforms you can use them for free if you have a small of loans needed for small purposes for up to 4 machines for example cluster
nodes 1 you want you want really scale that uh gets very also very expensive and and that's why it's good but there are also free alternatives especially for researchers to use and
what they try also to focus on and that it's an open infrastructure everybody can act components can integrate them can provide them as Doppler images so it's not limited to us as a consortium but we want to and able and more and more we hope in the future there will be action more projects not just always and which contribute and take up the platform and and help sustain it 1 of the line at
the time so far evidence from the but OK that and as I
mentioned 1 a master produce demonstrating the value in real world use cases and the developed 7 pilots and the I'm not talking about each 1 of them now but
showing you how 1 and mobility area so long
which allows end users to know how to observe and the use of the platform and it also integrates of course and other components of which are more domain specific also we have
health for example this open facts you open fact existed before but the I lifted the OpenText platform to this big data your architecture and for that agriculture energy you see there are different
architectures in the end of they use as a basis the big data platforms and then create the domain specific instantiations and to illustrate that the but
going to a show you some some bird some information about the year of Mobility pilot which we develop in the region of Macedonia and Greece and in the municipality of Thessaloniki for the several organizations the served as a partner and also Big Data Europe
and research institutes and especially in this mobility transport area and they have good context another number of another
organizations that on different areas so there's the central area of salinity in the urban area and the
peripheral area on the map and then
there are a number of data sources using a pilot as sensors of which are along the road so which tracked my ideas of blue cruise of the detectors so therefore million detections per week and then there are 25 unique devices detected per day and and you can track basically the troops of course not everyone has speech on but maybe 1 per cent of the people have such on or a 3 per cent and then you can improve
interpolate and use that and in order to form of and that's and in a way and animals because you cannot really associate a demand Eddie was a concrete persons
and and then there's a dynamic sensors so there's free floating car data are provided by a taxi fleet of Morton dollars 200 vehicles and also social media Twitter and Facebook is used so I show you here
for example is that free floating car data and features of
ingested and used in the platform every 100 meter meters there's some impulse generated providing some data on covering a large part of the day and then you can basically see and the it and frequency of of taxi ride for
example and where they are located on of the orientation this leads of the times on the stand and enriches on of the Texas currently located
share some some examples of the state of
and then some of our heroes and the analysis speech and derive the maximum average
minimum speed profiles along the road of 2 kilometres and if you have been to it as a limiting the public transportation is not as developed so road transportation is very important and then optimizing that making making it so on the better for for users is is quite quite important challenge of for for citizens to go on to get from a to B and you can see here for example traffic lights and can analyze that and
maybe optimize your the traffic lights or also according to the to the data according to other the day of the week the time of the day and so on and so on
here and then data from social media is also integrated so I can check in is intense alone the keys of the people submit their
location and and you can associated with different activities they're doing there are restaurants and public places attractions and
evaluate the state come and you can see by combining all these different
sources of we really have so these also different different kinds of check
ins of Twitter data and verb people tweet about things that are happening
in in the city and by
combining all these data sources can do a number of services so you can estimate travel time for example you can detect mobility patterns what typically happens on certain certain times of the day or certain days of the week and then you can organize the world for example the the traffic flows strategies the traffic lights and
so on some some other and of traffic management patterns can be or traffic management strategies can be implemented the so you can do estimations of Route Choice Models of calibrated that's a macroscopic traffic models microscopic traffic model of what our partner certain on the detect hazards there are certain peaks so for example in communication can be also useful contracted cat catastrophes
a situation of some kind of personal services and and that's what this what this pilot is about
and in terms of and sustainability in the medium and this is 1 the 1 of the last slides the following a giant inspired distributed development model and so on and of course we have partners located in different cities countries of the
development team has sold to work together so that we cleave of telephone conferences for example of the use of make extensive use of of each and of version control uh I think everything the developers available also in the top and ends in terms of sustainability cooperate with the big data value as as they just had a 2 weeks ago also about mRNA and of big data about associations contractual partner after the date PPE he there there is a large community of more than 100 organizations throughout Europe working on big data topics being a stakeholder and and you want to involve them and in the way we organize workshops hangouts maybe as of which you are all invited to and the altered our
further projects may be of some of you want to start a project in 1 of these areas or applying the platform to your particular use case uh this is very welcome on and here's a a
sorry for the from the scramble slides and it'll should have generated a pdf that probably is the the for foundation year are so this is the schedule for this year's workshop but you can find them all also on the website think that Europe but you work
on the and some of them
participated in these webinars for example to get more detailed information also related to the specific societal challenges thank you very much for your attention and i'm happy
to take questions you
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Hasard <Digitaltechnik>
Mustersprache
Schätzung
Strategisches Spiel
Modelltheorie
Druckertreiber
Simulation
Mittelwert
Systemidentifikation
Telekommunikation
Besprechung/Interview
Dienst <Informatik>
Information
Computeranimation
Informationsmodellierung
Datenmanagement
Distributionenraum
Mustersprache
Schätzung
Hasard <Digitaltechnik>
Druckertreiber
Auswahlaxiom
Schätzwert
Hasard <Digitaltechnik>
Routing
Katastrophentheorie
Dienst <Informatik>
Auswahlaxiom
Matrizenring
Software
Strategisches Spiel
Computerunterstützte Übersetzung
Modelltheorie
Simulation
Assoziativgesetz
Subtraktion
Selbst organisierendes System
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Datenmodell
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Dienst <Informatik>
Information
Term
Computeranimation
Rechenschieber
Software
Informationsmodellierung
Assoziativgesetz
Softwareentwickler
Maßerweiterung
Chipkarte
Web Site
Kontrollstruktur
Besprechung/Interview
Dichte <Stochastik>
Systemplattform
Computeranimation
Rechenschieber
Scheduling
Flächeninhalt
Energiedichte
Computersicherheit
Projektive Ebene
Ereignishorizont
Software
Besprechung/Interview
Dienst <Informatik>
Information
Information
Computeranimation

Metadaten

Formale Metadaten

Titel Big Data Europe - The collaborative creation of an open software platform for researchers addressing Europe's societal challenges
Serientitel 2nd Conference on Non-Textual Information: Software and Services for Science (S3), May 10-11, 2017 in Hannover
Teil 8
Anzahl der Teile 13
Autor Auer, Sören
Lizenz CC-Namensnennung 3.0 Deutschland:
Sie dürfen das Werk bzw. den Inhalt zu jedem legalen 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.
DOI 10.5446/31034
Herausgeber Technische Informationsbibliothek (TIB)
Erscheinungsjahr 2017
Sprache Englisch

Inhaltliche Metadaten

Fachgebiet Informatik
Abstract The management and analysis of large-scale datasets - described by the term Big Data - involves the three classic dimensions volume, velocity and variety. While the former two are well supported by a variety of software components, the variety dimension is still rather neglected but crucial for the integration and analysis of heterogeneous scientific data. In this talk we discuss the principles of Linked Research Data and practical approaches for their implementation using semantic technologies such as the Data Spaces or Knowledge Graphs. As a concrete example, we discuss the collaborative creation of the BigDataEurope Platform, an open software stack for researchers addressing Europe's societal challenges.

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