Use Case-Geo-spatial Cloud

Summary

In this discussion on geospatial cloud computing, the focus is on understanding the role of cloud infrastructure in managing, integrating, and analyzing spatial data. Geospatial data, which encompasses location-based information, is increasingly being used for various decision-making processes. The presentation explores the complexities of managing vast amounts of spatial data, the challenges of interoperability, and the benefits of using a cloud environment for spatial data management. This approach aims to provide seamless access, integration, and processing of spatial data across different platforms and organizations, highlighting the potential of spatial data infrastructure in enhancing geospatial analysis and decision-making.

Highlights

• Geospatial data is vital for decision-making processes involving spatial information. 🌐
• Using cloud infrastructure for spatial data offers scalability and flexibility. 📈
• Challenges in spatial data include large volume, interoperability, and variability. 🤔
• Cloud solutions facilitate shared resource pooling and ease of access. 🤝
• Implementing cloud services can significantly enhance disaster management and urban planning. 🏙️

Key Takeaways

• Geospatial cloud computing enables seamless integration and management of location-based data. 🛰️
• Cloud infrastructure is essential for handling the vast amount of spatial data efficiently. ☁️
• Interoperability and data standardization are crucial for effective geospatial data use. 🔄
• Spatial data infrastructure offers democratized access to geospatial information. 🌍
• Cloud solutions can minimize costs and resource intensive GIS operations. 💰

Overview

Geospatial cloud computing represents a fusion of geographic information systems (GIS) with cloud technology, aimed at facilitating the handling and analysis of spatial data efficiently. This approach addresses the growing need for managing massive datasets that represent geographical information, which can be static or dynamic in nature. By leveraging cloud infrastructure, organizations can adopt a scalable and flexible platform for storing, processing, and accessing geospatial data, crucial for applications ranging from navigation to disaster management.

The incorporation of cloud-based solutions in geospatial analysis provides key advantages, including resource pooling, cost-effectiveness, and widespread accessibility. Organizations and governments can benefit from the democratized access to geospatial data, allowing for more informed decision-making across various fields such as urban planning, environmental monitoring, and emergency response. Moreover, by standardizing data formats and ensuring interoperability, cloud solutions ensure that diverse datasets can be integrated and utilized more effectively.

Despite the apparent benefits, there are challenges associated with geospatial cloud computing. These include ensuring data security, handling varied data formats, and maintaining interoperability between different datasets and services. Nevertheless, ongoing advancements in cloud technology and spatial data infrastructure development provide promising prospects for overcoming these hurdles, cementing the role of geospatial cloud solutions as a pivotal component in the spatial data landscape.

Chapters

• 00:00 - 00:30: Introduction to Geospatial Cloud In this chapter, the discussion focuses on cloud computing with an emphasis on its application in geospatial contexts. The introduction sets the stage for understanding how spatial cloud technology, specifically geospatial cloud computing, functions and its typical use cases. The chapter aims to provide a foundational understanding of leveraging cloud technology for managing and analyzing geospatial data.
• 00:30 - 01:30: Understanding Geospatial Data The chapter titled 'Understanding Geospatial Data' discusses the increasing popularity and essential nature of geospatial data, which involves spatial information about the earth's surface. This information is often tied to a coordinated system with location-based data. The chapter emphasizes how this type of data is becoming more prevalent in everyday use.
• 01:30 - 02:30: Challenges of Centralized Data Storage The chapter discusses the increasing importance and challenges of managing centralized data, particularly spatial data used in location-based services and navigation. As data grows in volume and is stored by multiple organizations, integrating this data becomes crucial.
• 02:30 - 03:30: Spatial Cloud Solutions The chapter titled 'Spatial Cloud Solutions' discusses the integration of ground-level information in decision-making processes, especially for governmental and state agencies. It highlights the need for storing data centrally to facilitate querying and efficient access, which aids in development programs and various decision-making tasks.
• 03:30 - 04:30: Characteristics of Geospatial Data This chapter discusses the complexities and challenges related to geospatial data, highlighting the issues of centralizing data repositories when there are multiple stakeholders or providers involved. The text emphasizes the cost-intensive nature of managing geospatial data, noting the substantial resources required in terms of expertise, manpower, and funding for data collection and maintenance. The chapter points out that relocating data can change the dynamics of how it's managed and used.
• 04:30 - 05:30: Sources and Handling of Geospatial Data The volume of geospatial data is vast, posing challenges for data transfer.
• 05:30 - 07:30: Components and Challenges of GIS The chapter discusses the various components and challenges associated with Geographic Information Systems (GIS), highlighting global efforts in this domain. It focuses on the feasibility of utilizing spatial domains in cloud computing and mentions ongoing work at IIT Kharagpur.
• 07:30 - 09:30: Spatial Data Infrastructure and Cloud Benefits The chapter titled 'Spatial Data Infrastructure and Cloud Benefits' discusses the synergies between cloud computing and spatial data. It begins by explaining key cloud computing benefits such as on-demand service, ubiquitous access, resource pooling, location independence, rapid elasticity, and measured services. These features make cloud computing a powerful tool for managing spatial data. The chapter then transitions to focus on geographic information, particularly geospatial information which is data linked to specific locations on the earth's surface. It emphasizes the significance of spatial data in utilizing cloud resources effectively.
• 09:30 - 11:30: Geospatial Cloud Architecture The chapter titled 'Geospatial Cloud Architecture' discusses the significance of location-based data in various contexts. It emphasizes that a substantial portion, over ninety percent, of data is tied to specific locations. The transcript uses the example of student data, indicating how it includes not just general information but also specific location-based details such as the hall where a student resides or the department they are enrolled in. This highlights the pervasive nature of geospatial data across different domains.
• 11:30 - 14:30: Experimental Applications and Challenges The chapter titled 'Experimental Applications and Challenges' delves into the inherent presence and significance of spatial information in our daily lives. It emphasizes that spatial data, such as location-based information like hometowns and pin codes, is omnipresent and often an implicit aspect of various databases. Despite its ubiquity, spatial information may not always be actively utilized, but its role in denoting specific geographic regions and earth surfaces is acknowledged.
• 14:30 - 15:00: Conclusion The Conclusion chapter discusses the nature of information, categorizing it as either static or dynamic. Static information refers to data that remains unchanged over time or changes very slowly, such as the boundaries of states, districts, or natural features like parks and lakes. On the other hand, dynamic information is characterized by frequent changes, like traffic patterns or city population, which fluctuate over time.

Use Case-Geo-spatial Cloud Transcription

• 00:00 - 00:30 hello so we will continue our ah discussion on cloud computing and today we will talk about a ah typical use case of ah spatial cloud or more spaci specifically geospatial
• 00:30 - 01:00 cloud right so what we mean by this ah geospatial data and information and type of things so what we ah see these days that this spatial information information about earth surface and both ah with coordinated system right ah with location based information are becoming extremely popular we are ah we all of us are more or less ah used to or using day in day
• 01:00 - 01:30 out these ah location based services or navigation services ah for finding trajectories from one location to other and type of things right so all these are possible because there is a ah there are spatial data ah which are being maintained by ah various organizations right as data is becoming more heavy with and multiple organization a storing this data so there is a need that how to integrate this data
• 01:30 - 02:00 for decision makings purpose right one navigation maybe one of the things there are several other decision making things specially different development program of ah government and ah government agencies or ah state agencies and type of things which need require some sort of a ground level information to be integrated right so one way of looking at it that i store the data in a central place and then query on the look at the things right but ah if
• 02:00 - 02:30 there are multiple stakeholders of the data or multiple providers of this data then these making it is a central repository sometimes become a challenge right first of all the data itself is cost intensive if there are expertise and there are ah manpower there are fund involving a collect ah in collection and ah maintenance of the data so once you put the data in some other place then the whole thing goes for a ah different type of
• 02:30 - 03:00 management right secondly that the volume of data is enormous so this sort of ah transferring of the data over things may be extremely difficult right so ah so what what is what one of the solution what this ah spatial ah science or spatial ah management and things are looking at whether we can migrate to a cloud right its its some sort of a a cloud sort of infrastructure ah which can be provided on the ah on this ah
• 03:00 - 03:30 um platform right so this is a lot of ah work going on across the globe and we would like to see that what is the feasibility what sort of things are they in here and iit kharagpur also we have a small group working of those this sort of activity so we will try to see that what is the feasibility of this sort of domains spatial domain going to the cloud right so if you look at so this is this is all ah all we are discussing ah
• 03:30 - 04:00 for long ah like what cloud keeps a on demand service ubiquitous access resource pooling location independence rapid elasticity measured services right and so this is one part of the cloud side if you look at the geographic information side or the information spatial information or geospatial information side so information explicitly linked to the location on earths surface we are primarily looking at the spatial data which is looking ah specifically
• 04:00 - 04:30 on the earth surface right so it is a code dine driven thing so everything every data has a location incidentally if you see ah ah there was a report that more than ninety percent of our data are somewhere other related to some location right suppose if i see our student data here so along with lot of other things it says that which hall he or she is staying ah which department he or she is studying right where he is or
• 04:30 - 05:00 his or her hometown so all those things has a a poi a location on the earth surface so somewhere other with this spatial information is inherent or it is there in all our day to day life not only that in our most of our databases also right we may not be using it but ah its there if there is a pin code it says that which region it is referring to right so its explicitly earth surface geographic
• 05:00 - 05:30 information can be static or dynamic like static we does not change with time or changes over a long period of time like it may not be ah like a a a boundary of a state or a boundary of a district or a or sometimes in park lake etcetera they are not changing je day in the out are there are can be dynamic like some of the say traffic movement or population of a particular city which varies over time right there are lot of influx during data
• 05:30 - 06:00 in people are going out during night time and type of things if i look at this particular building where we are sitting here at now and so it has the if you look at the ah building wise it is ah static right or we can say pseudo static it is not changing ah day in day out but if you look at that ah food fall on the building that is changing right or even electricity need of the things may be changing so some of the data can be static some of
• 06:00 - 06:30 the things can be dynamic and there is a typical thing this geographic data varies over ah in scale right so information can range from meters to globe right some of the data if i want to point its it needs say ah up to a meter scale right find out that what is the location of a lamppost right or but it is can be higher the basically the map of a country or map of a district or map of a of a state may be much larger scale as so
• 06:30 - 07:00 ah and scale versus detail ah and ecological fallacies and those things are there so with the scale things goes on changing right like if i look at a building from a higher from the sky it is a point but if i close down it becomes a polygon and the with the scale the overall nature may change so these are some of the characteristics of the data right and ah if you look at that common geographic data what we ah which we are accustomed with
• 07:00 - 07:30 there can be some legal data that is ah location at these boundaries etcetera political maps cultural climatic ah some economic etcetera so there can be n number of such structure and ah there can be different agencies who are collecting the data or who are maintaining the data that can be ah some sort of social survey there are national survey like we have survey of india maps etcetera there can be some remotely sense sensor bits things or
• 07:30 - 08:00 ah [sin/sensor] sensor ah data like it can i can have ah remotely sensed data like air photos photographs taken from the air or satellite images even ah reporting by weather stations ah collected by gps and ah map marking associated with some attribute interest there can be pollution sensors across the things which gives data with locations where things are there there can be air quality control pollution sensor temperature sensor and other type of
• 08:00 - 08:30 atmospheric sensors switch gives data so these are different sources of data incident with these data are maintained by different organization by their proprietary nature and when i want to query of one more than once such data that i may need to see our or interoperate between the data right where ah this we need to see that where this cloud will be ah make some sense where using it so if we look at some of the formal definitions
• 08:30 - 09:00 like ah for capturing its a capturing storing querying analyzing and displaying geospatial data geographic information system or tools or platform which allows you to spatial data information related explicitly or implicitly for that surface so we are ah not going into deeper things just to show that why what sort of data just to have a idea of the and it can have hardware required hardware software data management is a major part spatial data
• 09:00 - 09:30 which is has a different type of characteristics both volume wise meaning wise and type of things and you require a special category of people who can operate on this data right so that is what we say people who can work on those type of things so these are the different component and what are the major challenges which are faced by this sort of huge spatial data it is data intensive it is computation intensive if i want to find a ah shortest path from a to b where the number of ah roads etcetera are ah like if i look at the ah representative
• 09:30 - 10:00 as a road network as a graph so huge ah volume of the things ah of this graph then it is extremely computationally intensive not only that if i want to find out a ah some sort of a overlaying type of things then also it is computationally intensive so variable loads on the gis server demands dynamic scaling in and scaling out of the resources and another interesting thing it is not like that always you are doing what so data is there whenever
• 10:00 - 10:30 the query comes you are doing and the that it may also varies depending on the things so ah we may require scaling in and scaling out of resources right so that will be advantageous that when i have huge computation i scale ah bring in resources when i dont require i release those resources type of things so there is a chance of having this sort of utility based computing or cloud sort of computing here and uses number of time uses network
• 10:30 - 11:00 intensive web services as we have discussed web services are services which are ah based on this ah um service oriented architecture and instead of ah data driven we have a service driven so we are talking about spatial web services which takes care of spatial data type of things so it is extremely network intensive because the data at very place maintained by different organization and you need network intensive thing
• 11:00 - 11:30 so if we look at a typical batch of land so if you see the spatial data there can be hydrological data there can be elevation data soil data infrastructure same place and we require a referencing system right the means based or the well known referencing system what we understand from our school days is the lat long it can be other referencing system which basically try to pin the data to a particular location and ah if this is not only true for
• 11:30 - 12:00 spatial data is true for ah any sort of data repository like we have operational data then summarizes making schema then metadata information of the schema which contains not only this [sc/schema] schema information lot of other things and there are business rules which are which the finally the end user wants right ah that i want to know from location kharagpur iit kharagpur to going to say iit chennai what should be the root or iit kharagpur so
• 12:00 - 12:30 some place in kolkata so i am not bothered about that how where data is stored how they are maintained ah i am i am more bothered about that what should be my path and what are the ah pois or the point of interest across the path and whats how i should move and what may be the expected time to reach that things what are the congestion level etcetera so i am more interested in the service rather than the actual data itself right so these are required so that is my that is my business rule that i want to find the shortest path
• 12:30 - 13:00 i may want to find a particular corridor of the things like if i if i make a say ah if if one man makes a canal so what should be its catchment area how much it will resolve so it says that if there is a canal is so much flowing water is there then i want to see the means if the if the the rule is that it says that ah both side two kilometer buffer will be the catchment area where the that water can be served so out of this two kilometer
• 13:00 - 13:30 how many population has been served how much agree ah means land agricultural land has been served can be the ah way or means can be my calculation so that is my business so the end user is more ah at the top of the thing and this is true for all sort of data and we have some of the verticals like data security which is a into end there are quality of service how reliable there can be management services and different other verticals which are which goes from the different aspects and what we
• 13:30 - 14:00 ah try to see in case of ah specially in case of this sort of spatial data of geospatial data we want to have some sort of a homogeneous way of looking at it like that different organization keeping that data when i query i require them in the same ah type of format same type of means both syntactic and semantic wise some sort of a homogeneity otherwise i cannot query on those data right so some references system is different then
• 14:00 - 14:30 i cannot quarry on the data if the scales are different then and so i cannot query data right so that ah we need to resolve issues like data description that where homogeneous i should understand standard encoding of the data standard mechanism of the data sharing so all this what it says that some sort of a ah a computing centralized computing facility which are like cloud type of things which can be helpful for the things right so ah
• 14:30 - 15:00 though i can have these separate type of data like it may be ah some data of the street building vegetation but some sort of a homogeneous integrated data i am looking for because i need to query on this ah data and we have seen that the trend is now more of a ah starting from the project mode now of a more societal mode that means data is available to ah everybody ah based on because it end of the date is taxpayers money so ah based on the ah considering policy securities etcetera but it ah it should be available
• 15:00 - 15:30 somewhere ubiquitously so that may be a cloud may be a solution another concept came ah came up or another very popular in ah advance country or also coming becoming popular or becoming a need in our country also its a making a spatial data infrastructure so looking the data as a infrastructure so whenever i want to query on the data i hook into this infrastructure right so its a its a infrastructure so some sort of analogous i can ah we can
• 15:30 - 16:00 look at that our telecom infrastructure right you have a there are different different ah stakeholders and ah there have different type of say policies or plans etcetera but i have a overall communication infrastructure somewhere other if i hook into the things like if i have a sim card with particular plan then i can access ah voice ah data and multimedia
• 16:00 - 16:30 type of things right based on again ip as i go so but as such that telecommunication is infrastructure right so whether i can have a spatial data energy infrastructure if it is there then i can do lot of things like i can do say planning for ah like what it what comment does for ah um say development planning thing for developing a particular area sitting on this school to some disasters management also right otherwise you need to always collect data put somewhere and do this so this sort of infrastructure
• 16:30 - 17:00 is the need not only it is coming becoming true for our country also slowly so in other sense what we need there can be different way of accessing the data there can be different instances of ah repositories so what we harvest here is more of a ah web services or spatial web services which can take requests from here and talk to the back in databases and
• 17:00 - 17:30 serve that it right so what we see that huge volume of data and metadata need of services and service orchestration because the if the ah if i my business rule may not be a just find a shortest path i can have a things which has a a series of services or the processing service to be initiated evolving standard and policies need for geo ah this may this basically lead to a need for a spatial cloud right or geospatial cloud
• 17:30 - 18:00 so there are as already we have discussed this ah point must as to look at so private public organization want to share their spatial data so different requirements for spatial data space network bandwidth etcetera based on the data volume ah get benefits from other things sharing the things less infrastructure and spatial service expertise needed right there is another important thing right so if i want to store if i am a ah organization
• 18:00 - 18:30 of collecting data and storing so i require a infrastructure to store there the data are volume us data ah are ah requires a different type of bandwidth etcetera and a i need a processing thing and not only that for that i require separate expertise on manpower do that so if i have a ah and as we understand that every infrastructure goes ah off or in the sense has need to be renewed every three to five years so it it is sustainability of
• 18:30 - 19:00 these things become say extremely difficult so instead if i if i hire infrastructure for my purpose that can be a more beneficial thing right so ah sometimes our organization lacks who are more specialized in spatial data lacks in maintaining the infrastructure and ah gis decisions are used ubiquitously in various government same government and private things and if we look at this ah spatial cloud geospatial
• 19:00 - 19:30 cloud so it support shared resource pooling which is useful for participating organization commons or shared goals as we are discussing a couple of slide back that it may be bumpy the requirement that means i need to scale up scale down on my resources so its a say shared resource pooling will be there and choice of various deployment service business model to base suit organization requirement managed service prevent data and work loss from the frequent outage etcetera like so if it is having a more many service and
• 19:30 - 20:00 which may minimize my financial constraint etcetera provide controls in sharing data with high security provision of the cloud as cloud also looks at the security features and looking aggressively on the security features so and security always come with a lot of cost ah so all we ah can leverage on the things and it can be hum ah much what we say ah economical to use those type of things and organization can acquire web service space as per their
• 20:00 - 20:30 need so if i if i need only this sort of spatial services i only harvest those services right i or if i need this type of data i only look for this data so it is customized based on the need from the infrastructure right this is the nist definition i just repeated so already we know and cloud advantages also is known to us that scalability on demand minimize i t resources improving business processes and all these ah advantages ah minimize
• 20:30 - 21:00 startup cost consumption base building or pay as you go billing pay as you go model economy of scale green computing these are all is a a need for these spatial data infrastructure or spatial data management and type of thing so geospatial data infrastructure so that means it is it is somewhere it is suited for this saito of things right and
• 21:00 - 21:30 this also we have seen that there are who are the cloud actors like one we have cloud service csp or broker customer ah is the other end negotiator is can be optional that negotiate between cloud and sla manager of security auditor so if you look considering all those things and if we look at a ah typical cloud architecture for geospatial data ah this architecture may be true for any type of other services but ah if we look at the geospatial data where
• 21:30 - 22:00 the geospatial or spatial services are put into place so this overall things come into play so there is a ah once a customer or a user or a service consumer comes into play so there is a negotiator which there can be number of brokers where which broke where which which basically broker ah acting as agent for different data or different sets of data there can be the same data may be handled in two repositories may so happen
• 22:00 - 22:30 and we have ah different data centers which gives vms and there are different organizations which launch their data and services on this vm and these are being ah negotiated and connected with the end user they are other than these we require sla manager to see that ah that ah this is slas or survey level agreements are not violated or things and there can be
• 22:30 - 23:00 a auditor or ah auditor to see that ah whether this sort of ah ah policies ah in terms of slas there can be security auditors security policies etcetera are being ah maintained in the this type of situation so i require a ah brokering service or who broke for the things there are different data centers which hosts this ah spatial data of various organizations and there can be sla there will be sla manager and auditor which
• 23:00 - 23:30 takes care of the overall management things there can be other component of the thing other different components ah there can be separate auditing services etcetera but this is the overall broad infrastructure so ah if i look at ah some sort of interface gis or what we say some sort of a realization of this sdi which gives which gives spatial
• 23:30 - 24:00 services what do you mean by this interface gis is that it itself does not have any data ah per se but it basically allows the consumer or the customer to connect with different service provider so its its a some sort of a some sort of a cloud or some sort of a middle tire which connects those things right so ah resource service resource allocation manipulation data services interface services all can be ah
• 24:00 - 24:30 provided and we can see that a typical structure we have different instances of this there can be so this is my ah backbone hardware over there we have this hypervisor which emulate different ah virtual machines there can there in ah guest os over that i can have different type of instances of this spatial cloud right or instances of these spatial web services
• 24:30 - 25:00 which are there so i can say that if it is say that spatial ah web services for a particular organization may particular state may be particular district can be instantiated on the things right and then i work on the thing like if you if we try to look at a ah some sort of analogy may not be very good analogy so if i say that if i when i am using some ah what processing service on the cloud say google docs so what we are doing we are basically instantiating my ah domain on that ah particular google doc right somebody else is again instantiated
• 25:00 - 25:30 things but at the back end it is the same it is the basically the google infrastructure we are using where the hypervisor and its a basically software as a service so we are at a much higher level but it is instantiating my ah thing so here whether i can have spatial service instantiated for my ah particular so ah if we look at the geospatial cloud model
• 25:30 - 26:00 so web services is here also key technology to provide spatial services need to integrate data and heterogeneous back end data services ah service can be ah set data service can be inside or outside the cloud there can be that some of the services can be outside can be run through paas service model using paas makes load balancing etcetera we can make ah scaling transparent so what way of looking at it ah that a typical scenario where different organization are there and i have a i have a one coordinating
• 26:00 - 26:30 cloud which has the central repository which takes care that where the organization how they are working and type of things right where where the data is there so that we have already discussed need to integrate data in a unified format or homogeneity it needs to be instead or interoperated instead performance metric computation power network things etcetera need to be judged and there can be various data sources we just saw some snapshot that
• 26:30 - 27:00 we made it experimental geospatial cloud i should say ah in our at iit kgp on our own experimental cloud that meghamala where we have seen that which were and so on open stack open source cloud platform and ah its a in house cloud used its a private cloud to iit kharagpur and over that we have did some experiment so as such ah we are not showing that some performance but showing that what sort of things may be there so this is a particular
• 27:00 - 27:30 area is a area of a bankura districts which is ah which is a district in west bengal so if i have a highway which is supplied by say highway national highway authority and on the other hand ah if i have a local road network which is a more handle by the state or the district authority then how to query something which involve this now if you the the though the image may not be clear this are running into separate vm it is something one dot seventy
• 27:30 - 28:00 four here one dot seventy five ah on the fly i want to have a processing service which integrate right so ah taking this two i need i basically have a merged road network right or in other sense i have a i have a two services of the road i am pulling and having a a merged ah road or having the national highway what that and there we can basically now query for a shortest path on these things right which takes care both these national highway
• 28:00 - 28:30 and the state highway like if it is my two location and then i want to find out the shortest path it does on the ah on this combine things so though the though the application may be ah trivial if not trivial it may begin well known application but see two separate repositories we can pull on a service mode and which are running on two different vms and pull on a service mode and basically my business rule is finding the shortest path right i am not
• 28:30 - 29:00 neither interested in the whole data of west bengal or whole data of india ah of road data i am interested in that shortest path and which are the locations so this this is one sort of thing so other thing i can have again two sort of data where i have one a canal type of things on again same bankura district and a river data which can be in a different thing now ah i want to have a merged water network which integrate this canal along with
• 29:00 - 29:30 the river again these two are in the separate ah vms or separate cloud instances i have a merged data and i want to buffer on this merged data right so ah try to see that during heavy rain or flooding what are the things which if i if i if the business rule is that within five hundred meter it is vulnerable then ah how what is the vulnerable area of this particular buffer zone so again i have two deperate separate
• 29:30 - 30:00 data instances which are integrated on the fly and gives this query right so here we have experimented on a ah on a experimental geospatial cloud we have installed over our meghamala thing so there are several challenges here other than which are typically something are typically for the spatial data which are not there in other type of what we say non spatial data type of things so implementation of spatial database is a one of the challenge it requires a lot of ah not only space need to understand
• 30:00 - 30:30 the data semantics order scaling of the spatial ah databases right need for multi tenant thing policy management among these different tenants geographically situated ah backups if you want to keep data security some of the channels are also there for other type of things some are typically for this type of ah spatial and also we have a major challenge of interoperability right data level interoperability like one data should match with other while we are
• 30:30 - 31:00 doing say buffering type of things and things it can be a service level inerrability like two services can talk to each other security level in interoperability like when i have different policies on different data sets then what should be the security level inter ah interoperability so ah major security concern is the multi tenancy is already for other things and lack of complete control of data application services there are [sa/several] several other concerns which ah assets ah to be ah deployed on ah
• 31:00 - 31:30 cloud what is the value of these assets what are the different ways this asset can be compromised etcetera so there are different type of ah other concerns which we need to look at in ah if we if we look at the overall spectrum so this spatial or geospatial domain ah finds a immediate applicability or a huge applicability on this sort of a cloud thing so one of the
• 31:30 - 32:00 one of the good use cases which are which not only in our country in different organization in different countries how to make this heterogeneous data talk to each other for different type of ah ah citizen centric development planning and disastrous management is becoming a popular ah um aspects of special cloud and this is a could use case
• 32:00 - 32:30 thank you