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Master Thesis: Adaptive Recommendations Based on Spatio-Temporal Data Streams


Adaptive Recommendations Based on Spatio-Temporal Data Streams


In recent times mobile devices, i.e. smart-phones, IoT sensors, GPS equipment and wearables  are getting sophisticated in hardware. Hence, developers are allow to implement complex  software that helps to produce more than position location of devices such as GPS positions,  WiFi fingerprinting signals, or Bluetooth beacons. Intelligent mobile devices are often connected through Internet or any other network to Location-Based Services which stores the Geopositioning location of the entity together with useful information about the state of the object,  for instance, this can be distance of a trip, walking steps, etc.  In parallel, streaming analytics is being more popular over Big Data adopters that believe  stream analytics is the next step in the revolution of real-time processing and Big Data. MapReduce of Apache Hadoop was the first to tackle the problematic of processing large sets of data.  This process is called batch processing but it is not able to process data streams. However,  Apache Hadoop has built a big ecosystem that takes MapReduce architecture as the core of  any other implementation which runs on top of its architecture. Then, Stream frameworks like  Apache Spark, Apache Flink and Apache Storm took Hadoop architecture to solve Big Data  issues such as Velocity and Volume. Those frameworks offer libraries and extensions which  allow to use machine learning algorithms and data mining techniques within data streams.  Although, stream processing is able now to process large and fast data streams, there has to  be a method to reduce the dimension of data without losing information of this set of samples.  Therefore, feature selection is an important technique that helps to reduce high dimensional  datasets. This thesis will discuss one of those techniques in order to reduce dimensionality of  datasets and to identify the most suitable dimensions which helps to formulate models for the  construction of adaptable recommendations.  This work introduces the design and implementation of a system together with guidelines  for the development of adaptable recommendations based on spatiotemporal data streams. For  this reason, it is fundamental to be capable of managing heterogeneous data sources and unstructured information. Another main goal of this thesis is to produce meaningful knowledge  for improving real-time Adaptive Systems in the field of Location-Based Services.

Supervisor: Bersant Deva

Type:  Master Thesis

Duration: 6 months

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TU Berlin - Service-centric Networking - TEL 19
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