Download Reem.gis.16.ss - University of Minnesota

TO: ACM SIGSPATIAL 2016 Student Travel Awards Committee
SUBJECT: Recommending Ms. Reem Ali for the ACM SIGSPATIAL 2016
Student Travel Awards
Dear Colleagues,
It is a pleasure to strongly recommend Ms. Reem Ali for the ACM SIGSPATIAL
2016 Student Travel Awards. I have known Reem since Fall 2012 when she
started attending my research group meetings out of her interest in the spatial
computing field. She has now co-authored five papers and taken two graduate
courses with me. She was among the best students in each course. She is
currently developing a doctoral dissertation under my supervision. Her paper
titled "Supply-Demand Ratio and On-Demand Spatial Service Brokers" has
recently been accepted at the International Workshop on Computational
Transportation Science (IWCTS 2016) in conjunction with ACM SIGSPATIAL
2016.
Reem’s thesis explores scalable computational techniques for analyzing spatiotemporal big data from connected vehicles such as GPS-trajectories and engine
measurement datasets. She has investigated the problem of mining cooccurrence patterns of non-compliant engine behavior such as patterns leading
to high engine emissions or low fuel efficiency. This is an important problem due
to societal applications such as reducing greenhouse gas emissions to address
climate change and reducing primary energy consumption. However, this
problem is challenging due to the huge data volume and the non-monotonicity of
popular spatio-temporal statistical interest measures of association (e.g. cross-K
function) which renders the anti-monotone pruning based algorithms (e.g. Apriori)
inapplicable. To address these challenges, Reem proposed novel upper bounds
for the cross-K function and a Multi-Parent Tracking approach which can be used
for efficiently filtering uninteresting candidate patterns. For this work, Reem has
collaborated with Prof. William Northrop, a domain expert in the area of
thermodynamics, engine emissions and fuels at the Department of Mechanical
Engineering, University of Minnesota. He has collected engine measurements
from on-board sensors on two different bus engines in the twin-cities area. A
case study with the real world engine data showed the ability of the proposed
approach to efficiently discover patterns which are interesting to engine
scientists. The results of this work were published at the 14th International
Symposium on Spatial and Temporal Databases (SSTD 2015).
In addition, Reem has also explored the matching problem in on-demand spatial
service brokers where demand from mobile consumers on the road network is
matched to supply from service providers. Unlike related work in ridesharing and
spatial crowd-sourcing systems which only focus on maximizing the number of
matched requests/tasks, Reem's work aims at allowing the broker to keep the
"eco-system" functioning not only by meeting consumer requirements, but also
by engaging many service providers and balancing their assigned requests which
could become a priority during periods where supply highly exceeds the demand.
This problem is important because of its many related societal applications in the
on-demand and sharing economy (e.g. on-demand ride hailing services, ondemand food delivery, etc). Challenges of this problem include the need to
satisfy many conflicting requirements for the broker, consumers and service
providers in addition to the need to scale to a large number of requests in
megacities with millions of consumers and thousands of service providers. TO
address these challenges, Reem proposed several matching heuristics for
meeting these conflicting requirements, including a new category of service
provider centric heuristics. Experimental results showed that the proposed
heuristics can help engage more service providers and balance their
assignments while achieving a similar or better number of matched requests and
that as matching heuristics have different dominance zones, a supply-demand
ratio aware broker is needed to select the best matching policy.
In summary, Reem is a very promising Ph.D. student and her thesis work has a
great potential for allowing efficient analysis of big spatio-temporal data from
connected vehicles. I believe that attending the ACM SIGSPATIAL GIS 2016
conference will be an excellent opportunity for her to get feedback and new
insights into her work from researchers in the spatial community which will allow
her to improve her thesis. Therefore, I strongly recommend Reem for a full travel
assistance through the ACM SIGSPATIAL 2016 Student Travel Awards.
Please do not hesitate to contact me if there are any questions.
Sincerely,
(Shashi Shekhar)
McKnight Distinguished University Professor