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Big data: finding, refining and monetizing – Making it possible for wheelchair users to travel with BookSpecials
Over the last 20 years the travel industry has changed radically. Through online information sharing, now the whole world lies at everybody’s feet. However, this applies not for the 50-75 million wheelchair users in the world. They are struggling a lot with organizing their day trips and vacations. As a result, they go much less often away, much less far away and many times to the same (dull) destinations. If they want something else, they literally need to do months of research beforehand.
The online travel sites possess huge amounts of data. The problem for wheelchair users is that nothing is categorized or searchable. For example, there are reviews from wheelchair users available on TripAdvisor, but it is impossible to search for them. And when there is data available, it is mostly wrong or incomplete. For example, about 10% of the vacation home rentals state that they are wheelchair accessible. In reality, probably only 2-5% of these actually are. This means that when a wheelchair user for example likes to rent an apartment via Airbnb, that this person probably needs to screen about 50 ‘wheelchair accessible’ listings to find only one that seems accessible indeed. And then, finding the right accommodation is only step one. Next is air travel, public transport, attractions, restaurants, etc.
This project assignment is about how to find, refine, combine, present and monetize all ‘publicly available’ travel information for wheelchair users.
“So that they can do a world trip by themselves as well.”
In today’s multi-channel customer environments, where customers use many different (communication) channels to solve outstanding questions and requests, it becomes increasingly difficult to optimize service levels and provide a memorable experience for customers.
Especially in a complex service environment as healthcare, with high involved customers, there are multiple contacts over the years. Primary concern towards their health care insurance; their health and that they can get the best (covered) service possible to stay healthy!
Underlined and CZ work together to build an agnostic environment in which all traceable, incoming and outgoing, customer contacts (e.g. call, web, e-mail, chat, etc…) are brought together into a unique dataset. This dataset is further enriched with relevant data that can be linked to (unique) customer events for analytical purposes to solve a number of questions how CZ can further build on their service level and optimization of the customer experience.
Onmi Design is currently working on the Do CHANGE project, funded in the Horizon 2020 program from the European Commission. In this project they are developing a health ecosystem that provides real-time behaviour change coaching based on input from various sensors. The student would assist in the development of analysis algorithms and integration of these algorithms in the system. For more information on the project please visit www.do-change.eu.
Sales of electric vehicles are on the rise. This poses a number of challenges for aging grids all across Europe by introducing new peak loads on already stressed grids. The problem is exacerbated by an increasing contribution of renewable energy sources. At the same time, EVs can also form part of the solution as mobile loads which offer flexibility to the grid by offering demand side management. In this context, it is necessary however to balance a number of (often competing) constraints foremost of which is meeting user specifications. These additional constraints include maximal self-consumption of renewable generation (e.g. solar), improving grid quality, taking advantage of market signals directly or by providing flexibility of consumption etc.
The optimization problem is tractable for a single vehicle but becomes more convoluted when applied to many (hundreds or even thousands of) vehicles, each with its own set of constraints. Furthermore, it is problematic both from a logistics (providing communication framework) and privacy (sharing user data) perspective to do completely centralized control. A number of techniques have been proposed to solve for this problem in a general setting ranging from classical optimization algorithms to meta-heuristics, distributed constrained optimization and multi agent reinforcement learning.
In the context of process mining, we are often confronted with companies willing to share their data if we can sufficiently anonymize this. However, to date, there are no well-defined plugins to do such anonymizations. Therefore, we are looking for a Master student that is willing to help us with this.
Part of the project will be an in-depth investigation in existing anonymization techniques. Which techniques are available and what analysis properties do they preserve. More importantly, how difficult is it to de-anonymize the data?
Another important part is the implementation of an anonymization framework in our toolset ProM. Good programming skills in Java are therefore important, no matter if you are a BIS or a CSE student.
Together with KPMG’s department on sustainability, we are looking for a Master student interested in text- and datamining. Part of KPMG’s job is to judge yearly reports on the topic of sustainability. This task is currently done by manually assessing the sentiment of a yearly report against the actual measureable sustainability values to see if the report is too optimistic or too pessimistic.
The goal of this project is to see if this can be done automatically through text- and datamining. KPMG has a large body of test and training data available for the student to start comparing reports. In depth knowledge of both supervised and unsupervised techniques will be needed for this project.
KPMG is a highly competitive company. Therefore, they are looking for a CSE or BIS Master student on track for a Cum-Laude (i.e. an average grade of 8). Furthermore, the student should have a strong interest in datamining. A selection process within KPMG is part of the hiring procedure.
Process mining of collaborative healthcare data - VitalHealth Software
|Mrs. Ine van der Ligt|
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