4 research outputs found
Mining Heterogeneous Urban Data at Multiple Granularity Layers
The recent development of urban areas and of the new advanced services supported by digital technologies has generated big challenges for people and city administrators, like air pollution, high energy consumption, traffic congestion, management of public events. Moreover, understanding the perception of citizens about the provided services and other relevant topics can help devising targeted actions in the management. With the large diffusion of sensing technologies and user devices, the capability to generate data of public interest within the urban area has rapidly grown. For instance, different sensors networks deployed in the urban area allow collecting a variety of data useful to characterize several aspects of the urban environment.
The huge amount of data produced by different types of devices and applications brings a rich knowledge about the urban context. Mining big urban data can provide decision makers with knowledge useful to tackle the aforementioned challenges for a smart and sustainable administration of urban spaces. However, the high volume and heterogeneity of data increase the complexity of the analysis. Moreover, different sources provide data with different spatial and temporal references. The extraction of significant information from such diverse kinds of data depends also on how they are integrated, hence alternative data representations and efficient processing technologies are required.
The PhD research activity presented in this thesis was aimed at tackling these issues. Indeed, the thesis deals with the analysis of big heterogeneous data in smart city scenarios, by means of new data mining techniques and algorithms, to study the nature of urban related processes. The problem is addressed focusing on both infrastructural and algorithmic layers. In the first layer, the thesis proposes the enhancement of the current leading techniques for the storage and elaboration of Big Data. The integration with novel computing platforms is also considered to support parallelization of tasks, tackling the issue of automatic scaling of resources. At algorithmic layer, the research activity aimed at innovating current data mining algorithms, by adapting them to novel Big Data architectures and to Cloud computing environments. Such algorithms have been applied to various classes of urban data, in order to discover hidden but important information to support the optimization of the related processes.
This research activity focused on the development of a distributed framework to automatically aggregate heterogeneous data at multiple temporal and spatial granularities and to apply different data mining techniques. Parallel computations are performed according to the MapReduce paradigm and exploiting in-memory computing to reach near-linear computational scalability. By exploring manifold data resolutions in a relatively short time, several additional patterns of data can be discovered, allowing to further enrich the description of urban processes. Such framework is suitably applied to different use cases, where many types of data are used to provide insightful descriptive and predictive analyses.
In particular, the PhD activity addressed two main issues in the context of urban data mining: the evaluation of buildings energy efficiency from different energy-related data and the characterization of people's perception and interest about different topics from user-generated content on social networks. For each use case within the considered applications, a specific architectural solution was designed to obtain meaningful and actionable results and to optimize the computational performance and scalability of algorithms, which were extensively validated through experimental tests
An e-business model for the participation of households in the Serbian electricity market based on smart grid technologies
ΠΡΠ΅Π΄ΠΌΠ΅Ρ ΠΈΡΡΡΠ°ΠΆΠΈΠ²Π°ΡΠ° Π΄ΠΈΡΠ΅ΡΡΠ°ΡΠΈΡΠ΅ ΡΠ΅ ΡΠ°Π·Π²ΠΎΡ ΠΌΠΎΠ΄Π΅Π»Π° Π΅Π»Π΅ΠΊΡΡΠΎΠ½ΡΠΊΠΎΠ³ ΠΏΠΎΡΠ»ΠΎΠ²Π°ΡΠ° Π·Π°ΡΠ½ΠΎΠ²Π°Π½ΠΎΠ³ Π½Π° ΡΠ»Π΅ΠΊΡΠΈΠ±ΠΈΠ»Π½ΠΎΠΌ ΡΡΠ΅ΡΡΡ ΠΏΠΎΡΡΠΎΡΠ°ΡΠ° Π½Π° ΡΡΠΏΡΠΊΠΎΠΌ ΡΡΠΆΠΈΡΡΡ Π΅Π»Π΅ΠΊΡΡΠΈΡΠ½Π΅ Π΅Π½Π΅ΡΠ³ΠΈΡΠ΅. Π¦ΠΈΡ ΡΠ΅ ΡΠ°Π·Π²oj ΠΎΠ΄ΡΠΆΠΈΠ²oΠ³ ΠΈ ΠΏΡΠΈΠΌΠ΅Π½ΡΠΈΠ²oΠ³ ΠΌΠΎΠ΄Π΅Π»a Π΅Π»Π΅ΠΊΡΡΠΎΠ½ΡΠΊΠΎΠ³ ΠΏΠΎΡΠ»ΠΎΠ²Π°ΡΠ° ΠΊΠΎΡΠΈ ΠΎΠΌΠΎΠ³ΡΡΠ°Π²Π° ΡΡΠ΅ΡΡΠ΅ Π΄ΠΎΠΌΠ°ΡΠΈΠ½ΡΡΠ°Π²Π° ΠΈ ΠΏΠΎΡΠ΅Π΄ΠΈΠ½Π°ΡΠ½ΠΈΡ
ΡΡΠ΅ΡΠ°ΡΠ° Π½Π° Π±Π°Π»Π°Π½ΡΠ½ΠΎΠΌ ΡΡΠΆΠΈΡΡΡ ΠΈ Π½Π° Π±Π΅ΡΠ·ΠΈ Π΅Π»Π΅ΠΊΡΡΠΈΡΠ½Π΅ Π΅Π½Π΅ΡΠ³ΠΈΡΠ΅ ΠΊΠΎΡΠΈΡΡΠ΅ΡΠ΅ΠΌ smart grid ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ° ΠΈ demand-response ΡΠ΅ΡΠ²ΠΈΡΠ°. Π£ΡΠ΅ΡΡΠ²ΠΎΠ²Π°ΡΠ΅ΠΌ ΠΏΠΎΡΠ΅Π΄ΠΈΠ½Π°ΡΠ½ΠΈΡ
Π΄ΠΎΠΌΠ°ΡΠΈΠ½ΡΡΠ°Π²Π° ΠΈ ΡΡΠ΅ΡΠ°ΡΠ° Ρ Π±Π°Π»Π°Π½ΡΠ½ΠΎΠΌ ΡΡΠΆΠΈΡΡΡ ΠΎΠΌΠΎΠ³ΡΡΠ°Π²Π° ΡΠ΅ ΠΏΡΠ΅ΡΠΈΠ·Π½Π° ΠΊΠΎΠ½ΡΡΠΎΠ»Π° ΡΡΠ΅ΠΊΠ²Π΅Π½ΡΠΈΡΠ΅ Π΅Π»Π΅ΠΊΡΡΠΎΠ΅Π½Π΅ΡΠ³Π΅ΡΡΠΊΠΎΠ³ ΡΠΈΡΡΠ΅ΠΌΠ°, ΡΡΠΎ oΡΠ²Π°ΡΠ° ΠΌΠΎΠ³ΡΡΠ½ΠΎΡΡΠΈ Π·Π° Π΅ΠΊΡΠΏΠ»ΠΎΠ°ΡΠ°ΡΠΈΡΡ ΠΎΠ±Π½ΠΎΠ²ΡΠΈΠ²ΠΈΡ
ΠΈΠ·Π²ΠΎΡΠ° Π΅Π»Π΅ΠΊΡΡΠΈΡΠ½Π΅ Π΅Π½Π΅ΡΠ³ΠΈΡΠ΅ ΠΏΡΠ΅ΠΊΠΎ ΡΠ»Π΅ΠΊΡΠΈΠ±ΠΈΠ»Π½ΠΈΡΠ΅Π³ ΠΈ ΡΠ°ΡΠ½ΠΈΡΠ΅Π³ ΡΠΏΡΠ°Π²ΡΠ°ΡΠ° ΡΠ»ΡΠΊΡΡΠ°ΡΠΈΡΠ°ΠΌΠ°. TΡΠΆΠΈΡΡa Π΅Π»Π΅ΠΊΡΡΠΈΡΠ½Π΅ Π΅Π½Π΅ΡΠ³ΠΈΡΠ΅ ΡΡΠ°Π΄ΠΈΡΠΈΠΎΠ½Π°Π»Π½ΠΎ ΡΡΠ½ΠΊΡΠΈΠΎΠ½ΠΈΡΡ ΠΏΠΎ B2B ΠΌΠΎΠ΄Π΅Π»Ρ Π΅Π»Π΅ΠΊΡΡΠΎΠ½ΡΠΊΠΎΠ³ ΠΏΠΎΡΠ»ΠΎΠ²Π°ΡΠ° Π³Π΄Π΅ ΡΠ΅ ΠΏΠΎΡΠ»ΠΎΠ²Π°ΡΠ΅ Π²ΡΡΠΈ ΠΈΡΠΊΡΡΡΠΈΠ²ΠΎ ΠΈΠ·ΠΌΠ΅ΡΡ ΠΏΡΠΎΠΈΠ·Π²ΠΎΡΠ°ΡΠ° ΠΈ ΠΎΠΏΠ΅ΡΠ°ΡΠΎΡΠ° ΡΡΠΆΠΈΡΡΠ°. ΠΠ΅ΡΠ΅Π³ΡΠ»Π°ΡΠΈΡΠ° ΠΎΡΠ²Π°ΡΠ° Π½ΠΎΠ²Π΅ ΠΌΠΎΠ³ΡΡΠ½ΠΎΡΡΠΈ ΠΏΠΎΡΠ»ΠΎΠ²Π°ΡΠ° Π·Π° ΡΡΠΆΠΈΡΡΠ° Π΅Π»Π΅ΠΊΡΡΠΈΡΠ½Π΅ Π΅Π½Π΅ΡΠ³ΠΈΡΠ΅, ΠΏΡΠ΅ΡΠ΅ΠΆΠ½ΠΎ Π·Π° Π΄ΠΎΠΌΠ°ΡΠΈΠ½ΡΡΠ²Π° ΡΠΈΡΠΎΠΌ ΡΠ΅ ΠΈΠ½ΠΊΠ»ΡΠ·ΠΈΡΠΎΠΌ Ρ Π²Π΅Π»ΠΈΠΊΠΎΡ ΠΌΠ΅ΡΠΈ ΠΌΠΎΠΆΠ΅ ΠΏΠΎΠ²Π΅ΡΠ°ΡΠΈ ΡΠ»Π΅ΠΊΡΠΈΠ±ΠΈΠ»Π½ΠΎΡΡ ΠΈ Π΅ΡΠΈΠΊΠ°ΡΠ½ΠΎΡΡ ΠΌΠ΅Ρ
Π°Π½ΠΈΠ·ΠΌΠ° Π±Π°Π»Π°Π½ΡΠΈΡΠ°ΡΠ° ΠΈ ΡΠ΅Π»ΠΎΠΊΡΠΏΠ½Π° ΡΡΠ°Π±ΠΈΠ»Π½ΠΎΡΡ Π΅Π»Π΅ΠΊΡΡΠΎΠ΅Π½Π΅ΡΠ³Π΅ΡΡΠΊΠ΅ ΠΌΡΠ΅ΠΆΠ΅. ΠΠΎΡΠ»ΠΎΠ²Π½ΠΈ ΠΌΠΎΠ΄Π΅Π» ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ Ρ ΠΎΠ²ΠΎΡ Π΄ΠΈΡΠ΅ΡΡΠ°ΡΠΈΡΠΈ ΠΏΡΠΈΠ»Π°Π³ΠΎΡΠ΅Π½ ΡΠ΅ ΠΏΠΎΡΠ»ΠΎΠ²Π½ΠΎΠΌ ΠΎΠΊΡΡΠΆΠ΅ΡΡ ΠΈ ΡΠ΅Π³ΡΠ»Π°ΡΠΈΠ²ΠΈ Ρ Π Π΅ΠΏΡΠ±Π»ΠΈΡΠΈ Π‘ΡΠ±ΠΈΡΠΈ. ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π° ΡΠ΅Ρ
Π½ΠΈΡΠΊΠ° ΡΠΏΠ΅ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡΠ° ΠΌΠΎΠ΄Π΅Π»Π° ΡΠ°ΡΡΠΎΡΠΈ ΡΠ΅ ΠΎΠ΄ Π΄ΠΈΡΡΡΠΈΠ±ΡΠΈΡΠ°Π½Π΅ ΠΈΠ½ΡΡΠ°ΡΡΡΡΠΊΡΡΡΠ΅ Π·Π° ΠΏΠΎΠ²Π΅Π·ΠΈΠ²Π°ΡΠ΅ ΠΈ ΠΊΠΎΠΎΡΠ΄ΠΈΠ½Π°ΡΠΈΡΡ Π²Π΅Π»ΠΈΠΊΠΎΠ³ Π±ΡΠΎΡΠ° ΠΊΠΎΡΠΈΡΠ½ΠΈΡΠΊΠΈΡ
IoT ΡΡΠ΅ΡΠ°ΡΠ°. ΠΠ° ΠΏΡΠΈΠΊΡΠΏΡΠ°ΡΠ΅, Π°Π½Π°Π»ΠΈΠ·Ρ ΠΈ ΡΠΏΡΠ°Π²ΡΠ°ΡΠ΅ ΠΏΠΎΠ΄Π°ΡΠΈΠΌΠ° Ρ ΡΠ΅Π°Π»Π½ΠΎΠΌ Π²ΡΠ΅ΠΌΠ΅Π½Ρ ΠΊΠΎΡΠΈΡΡΠ΅ ΡΠ΅ Π°Π½Π°Π»ΠΈΡΠΈΡΠΊΠ΅ ΠΌΠ΅ΡΠΎΠ΄Π΅ ΠΈ ΡΠΎΡΡΠ²Π΅ΡΡΠΊΠΈ Π°Π»Π°ΡΠΈ Π±Π°Π·ΠΈΡΠ°Π½ΠΈ Π½Π° ΠΏΠΎΡΠ»ΠΎΠ²Π½ΠΎΡ ΠΈΠ½ΡΠ΅Π»ΠΈΠ³Π΅Π½ΡΠΈΡΠΈ. ΠΡΠ΅Π΄Π½ΠΎΡΡ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΎΠ³ ΠΌΠΎΠ΄Π΅Π»Π° ΡΠ΅ Ρ ΠΌΠΎΠ³ΡΡΠ½ΠΎΡΡΠΈ ΠΏΡΠΈΠΌΠ΅Π½Π΅ Π½Π° ΡΡΠΆΠΈΡΡΠΈΠΌΠ° Π΅Π»Π΅ΠΊΡΡΠΈΡΠ½Π΅ Π΅Π½Π΅ΡΠ³ΠΈΡΠ΅ ΠΊΠΎΡΠ° ΡΡ Ρ ΡΠ°Π·Π²ΠΎΡΡ, Π½ΠΈΡΠΊΠΈΠΌ ΠΈΠ½ΠΈΡΠΈΡΠ°Π»Π½ΠΈΠΌ ΠΈΠ½Π²Π΅ΡΡΠΈΡΠΈΡΠ°ΠΌΠ° ΠΈ ΠΈΠ½ΡΠ΅Π³ΡΠ°ΡΠΈΡΠΈ ΡΠ° smart grid ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΡΠ°ΠΌΠ°. Π£ Π΄ΠΈΡΠ΅ΡΡΠ°ΡΠΈΡΠΈ ΡΡ ΠΏΡΠΈΠΊΠ°Π·Π°Π½ΠΈ ΠΈΡΡΡΠ°ΠΆΠΈΠ²Π°ΡΠ΅ ΠΈ Π°Π½Π°Π»ΠΈΠ·Π° ΡΠΏΡΠ΅ΠΌΠ½ΠΎΡΡΠΈ ΠΏΠΎΡΡΠΎΡΠ°ΡΠ° ΠΈ ΡΡΠ΅ΡΠ½ΠΈΠΊΠ° Π½Π° ΡΡΠΆΠΈΡΡΡ, Π·Π° ΠΏΡΠΈΠΌΠ΅Π½Ρ ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΎΠ³ ΠΌΠΎΠ΄Π΅Π»Π°