E2 - HW1 of Spatial Databases
1. Capacity Constrained Network Voronoi Diagram (CCNVD) - Service Center Allocation Problem
Sources:
1. Advances in Spatial and Temporal Databases - 13th International Symposium, SSTD 2013 Munich,
Germany, August 2013 Proceedings
2. http://www.informatik.uni-trier.de/~ley/pers/hd/s/Shekhar:Shashi.html
3. http://www.geocomputation.org/2013/papers/116.pdf
4. PPT from SSTD by authors
5. Capacity Constrained Voronoi Diagrams in Continuous spaces by Michael Balzer
6. Wikipedia - Voronoi Diagram
7. Video - http://graphics.uni-konstanz.de/publikationen/2008/capacityvoronoi/website/
8. PPT of Load Imbalance with Preliminary results - From UMN
Description:
It is one of the most important problems due to it’s application in the critical societal needs. The
examples of such application are assigning evacuees to shelters and assigning patients to hospitals.
CCNVD - Given a graph and service centers, Capacity Constrained Network-Voronoi Diagram (CCNVD)
partitions the graph into a set of contiguous service areas that meet service center capacities and
minimize the sum of distances (min-sum) from graph-nodes to allotted service
centers. (From the recent paper by Shashi Shekhar et al).
CCNVD is NP-hard and it is challenging to solve with the large size of the transportation network and
with the constraint in the problem that the service centers (SAs) must be continuous in the graph.
Some of the initial work included either giving preference to service center capacity constraints or
service area contiguity. Both have not been considered in optimally allocating service centers. Paper by
CS department at UMN considers both of these and tries to optimize the problem.
2. Spatial Cognitive Assistance
Sources:
1. http://www.spatial.cs.umn.edu/ccc/CCCSpatialComputingDraftTwo.pdf
2.
http://www.researchgate.net/publication/255729025_Spatial_Recognition_of_Activities_for_Cognitive_
Assistance_Realistic_Scenarios_Using_Clinical_Data_from_Alzheimer's_Patients
3.
http://books.google.com/books?hl=en&lr=&id=7AWMY0uz8awC&oi=fnd&pg=PA221&dq=spatial+cognit
ive+assistance&ots=gBubctMjLt&sig=e26UNkrjO0maQgvwmyXWuV6GYv4#v=onepage&q=spatial%20co
gnitive%20assistance&f=false
4. http://www.sfbtr8.spatial-cognition.de/ASCIAS/
5. http://arxiv.org/abs/1307.3040
6. http://www.spatial.cs.umn.edu/ccc/CCCSpatialComputingDraftTwo.pdf
7. http://old.nabble.com/From-GPS-and-Virtual-Globes-to-Spatial-Computing---2020-td35840093.html
8. http://read.dmtmag.com/i/99375/12
Description:
The first best example for spatial cognitive assistance would be the navigation system in cars. They assist
the drivers to reach their destinations with the help of spatial data available. The assistance services are
being personalized for many other users including public transit riders, bicycle users, wheelchair users
etc. The advancements have been trying to match more with the spatial cognition of the user. For
example routing information based on landmarks would be much better for a user when compared to
the street names.
Some of the challenges for spatial cognitive assistance specified in the literature are:
(1) Determining which cognitive skills are important to preserve, and which may be allowed to atrophy
(2) The trade-offs between task performance and skill retention (and robustness to disaster)
(3) Designing spatial cognitive assistance to improve the user’s knowledge and skills (not just immediate
task performance)
(4) Evaluating spatial cognitive assistance systems for improving the user’s spatial knowledge and skills.
(Taken from paper)
Some of the problems that the spatial cognitive assistance can cause are:
Making human forget the manual procedure to do a task & so deskilling them
Problems due to infrastructure failure
Investigations are being performed in the direction of how to avoid these problems while still providing
assistance to the users.
3. Bulk Loading
[1] introduces bulk loading as "the creation of index from scratch on a potentially large set of data." It
goes on to claim that bulk loading has its advantages in that properties of the data set may be obtained
in advance. These properties could benefit the design of efficient space-time partitioning index
structures that spatial databases often use, according to the authors.
[2] suggests the same definition for bulk loading as a process. It also states that less research has been
done on non-traditional index structures, especially those suitable for managing multi-dimensional data,
spatial data, or metric data, than on traditional index structures like B-tree. According to the paper, bulk
loading on traditional one-dimensional data is usually done through a sort-based method. First, the data
is sorted and then the tree is built in a bottom-up fashion. This is inapplicable, per the authors, to the
multi-dimensional data, in that it is always inappropriate to define any order for multi-dimensional data
for a traditional sorting algorithm. Two other approaches, namely, buffer-based bulk loading and
sample-based bulk loading, are also briefly described by the paper. The authors propose two generic
sample-based bulk loading algorithms that are claimed to be conceptually simpler yet offer better
performances.
[3] explains in its introduction the motivation for bulk loading, especially for non-standard databases
such as spatial databases. The authors suggest that the support of bulk operations like bulk loading on
multi-dimensional data will benefit data processing such as performing spatial join. It is also claimed that
very little research has been done in this area at the time. The paper proposes a generic algorithm for
multi-dimensional data bulk loading.
References:
[1] Spatial Databases, Wiley Encyclopedia of Computer Science and Engineering (Ed. Benjamin Wah),
John Wiley and Sons Inc, 2009, isbn 978-0471383932.
[2] J. van den Bercken and B. Seeger, An evaluation of generic bulk loading techniques, in VLDB '01: Proc.
27th International Conference on Very Large Data Bases. San Francisco. CA: Morgan Kaufmann
Publishers Inc., 2001, pp. 461-470.
[3] J. van den Bercken, B. Seeger, and P. Widmayer, A generic approach to bulk loading multidimensional index structures, in VLDB '97: Proc. 23rd International Conference on Very Large Data
Bases. pp. 406-415.
4. Parallel GIS
[1] introduces parallel GIS as geographic information systems (GIS) that use parallelization. According to
the author, this can be achieved either by function partitioning or data partitioning. Function
partitioning uses specialized data structures and algorithms while data partitioning typically divide the
data among different processors and execute the algorithms independently on each processor.
[2] introduces concepts, terminology, and technologies for parallel processing, with particular reference
to GIS. It also explores problems with designing parallel software for GIS applications, potential vector
and rastor data structures and details the algorithmic design for some major GIS operations.
[3] introduces the etymology of the term 'Distributed GIS', which we recognize as a synonym for parallel
GIS. It goes on to list some of the GIS services and standards of distributed GIS.
[4] describes, in its introduction, the evolving need to apply parallel computing technologies in the GIS
applications as the amount of data/users explode. The paper then goes on to introduce cluster-based
parallel GIS with the example of parallelization on GRASS GIS.
References:
[1] Spatial Databases, Wiley Encyclopedia of Computer Science and Engineering (Ed. Benjamin Wah),
John Wiley and Sons Inc, 2009, isbn 978-0471383932.
[2] Parallel Processing Algorithms for GIS, CRC Press; 1 edition(December 2, 1997), isbn-10 0748405097,
isbn-13 978-0748405091
[3] http://en.wikipedia.org/wiki/Distributed_GIS
[4] Fang Huang; Dingsheng Liu; Peng Liu; Shaogang Wang; Yi Zeng; Guoqing Li; Wenyang Yu; Jian Wang;
Lingjun Zhao; Lv Pang, "Research On Cluster-Based Parallel GIS with the Example of Parallelization on
GRASS GIS," Grid and Cooperative Computing, 2007. GCC 2007. Sixth International Conference on , vol.,
no., pp.642,649, 16-18 Aug. 2007
doi: 10.1109/GCC.2007.107