Συσταδοποίηση τροχιών κινούμενων αντικειμένων με χρονικούς περιορισμούς σε κατανεμημένες βάσεις δεδομένων - Σκαρλάτος, Ευστάθιος (Σχολή Αγρονόμων και Τοπογράφων Μηχανικών, 2016)
As sensors spread across almost every industry, the Internet of Things (IoT) is going to trigger a massive influx of big data. The Internet of Things revolves around increasing machine-to-machine communication; it’s built on cloud computing and networks of data-gathering sensors; it is a mobile, virtual, and instantaneous connection; and they say it’s going to make everything in our lives from streetlights to seaports "smart" (Burrus D., 2014). While, the Internet of Things is about data, devices, and connectivity; data – big and small – is in front and center in the IoT world of connected devices. Especially big data comes into large amounts, it is a mixture of structured and unstructured information, which arrives at (often real-time) speed and can be of uncertain provenance (Burrus D., 2014). As mentioned by Brown B., et al (2013) "just think about what could be happening at your own company right now: sensors embedded in process machinery may be collecting operations data, while marketers scan social media or use location data from smartphones to understand teens’ buying quirks. Data exchanges may be networking your supply chain partners and employees could be swapping best practices on corporate wikis" and you will understand the game-changing effects of big data. Over the last few years the volume of data has exploded. Heading more and more towards data centralized sciences, a variety of solutions have been implemented in order to find the appropriate one for the problem of large data (so-called Big Data) management. Big Data concerns large-volume, complex growing datasets with multiple and autonomous sources (Dinesh J., Patil B., 2014). While the volume of Big Data increases, so do the complexity and relationships underneath the data. Driven by real-world applications and key industrial stakeholders managing and mining Big Data have shown to be a challenging, yet not complete task. The critical challenge is using this data when it is still in motion – and extracting valuable information from it (Teachey D., 2015). The rise of Big Data is driven by the rapid increase of complex data and their changes in volume and nature. Talking about the nature of data, millions of dynamic data, data from sensors and social media, etc. are being collected, with or without containing spatio-temporal information. Specifically, mobile devices get to know more information about the user, day by day, and data gathered and recorded by such devices need to be collected, stored and analyzed appropriately in order to identify potential patterns and lead to useful conclusions. It is evident today that big data and especially data related to moving objects, appear to be our only choice if we want to progress and prosper. In particular, space-time position data, named trajectory data, may be used to record the trajectories of people or objects. With the increasing application of positioning and communication technological developments, the accumulation of large amounts of trajectory data provides opportunities for mining useful information from the data. Analysis and extraction of useful information from mass trajectory data have emerged as hot issues in data mining and it presents, still today, many challenges. Furthermore, in typical data mining systems, the mining procedures requires computational intensive computing units for data analysis and comparisons. For big data mining, because data scale is far beyond the capacity that a single personal computer can handle, a typical big data processing framework will rely on cluster computers with high-performance computing platform, with a data mining task being deployed by running some parallel programming tools on large number of clusters (computer nodes) (Wu X., et al., 2014). To adapt to the multisource, multiscale and dynamic Big Data, researchers have been exploring different techniques and expanding existing data mining methods in many ways in order to serve the need of the data. Severous techniques that have been developed and implemented, regarding Big Data mining processes and parallel programming programs are listed in this study. In particular, this Mater thesis aims to examine both the required theoretical background of the parallel programming models and management of distributed databases, as well as their application in spatio-temporal field of orbital data. Precisely, by integrated a procedural language like PL/Proxy, which allows the user to horizontally distribute data into nodes through functions in a database management system, PostgreSQL is being extended to a distributed database management system. (D-DBMS) Moreover, in this thesis the development of the simulation of the ReTraTree structure in a D-DBMS is described together with two alternative implementations of QuT clustering algorithm, proposing such a solution on top of ReTraTree structure, trying to provide a holistic solution to the problem of distribution and clustering spatiotemporal data. Finally, the required functions have been implemented for distributing sub trajectories intersecting a temporal period (hard and data driven partitioning) into nodes and clustering through S2T-Clustering algorithm for electing representative trajectories that will eventually form the leaves of the tree. Thus, concerning QuT-Clustering algorithm, two alternatives have been investigated: the collection of the representatives intersecting the period of interest, which have been distributed to the relevant nodes, their sort in time and partition in equivalence classes and finally, their comparison based on a metric function (Alternative implementation parallel QuT-Clustering), as well as the collecting of the representatives intersecting the period of interest, which have been distributed to the relevant nodes, their sort in time and partition in equivalence classes, the distribution of the classes to nodes and finally, the comparison of representatives within the classes.