Network Measurements and Traffic Monitoring
The importance of mobile Internet access in our society is steadily
growing, as more physical objects get connected (Internet of Things) and an
increasing share of our daily activities rely on smartphones and apps.
Ubiquitous and reliable connectivity is provided to the end-users by a “network
of networks”, i.e., a global system composed of the interconnection of multiple
IP network infrastructures, wired and wireless, owned and managed by different
players (network operators). The scale and complexity of the network
infrastructure is growing together with the requirements for reliability and
performances. Owing to the ever-evolving nature of such network
infrastructures, the risk of failures and performance degradation caused by
accidental faults and/or deliberate attacks is not decreasing. As with any
complex infrastructure, the network needs to be continuously monitored in order
to promptly identify (and resolve) problems, failures and any sort of anomalous
The course focuses on methods and techniques to measure the
performances of a packet-oriented network in order to reveal performance
degradation and faults in wide-area networks. Particular focus is given to
wireless mobile networks (3G/4G).
The goal of the course is to provide the student with an
understanding of the most important aspects that must be considered in all
phases of the monitoring process, from the design of the data collection
methodology to the representation, analysis and interpretation of the
measurement data. Some fundamental theoretical aspects of data analytics will
be discussed along with more practical “do and don’t” rules. During the course,
several case-study problems from real-world measurement activities and research
problems will be critically examined
and discussed in the classroom, in order to distill general guidelines and data
properties in a bottom-up fashion.
The course is useful also for PhD students that are working in
different research areas. In fact, the analysis of data from a dynamic,
heterogeneous and complex system like “the Internet” leads naturally to
encounter a number of concepts and system-level aspects that are very general
and apply to many other engineered systems.
In summary, the methods and skills acquired by the students during
the project will benefit the students along multiple directions:
Students will gain a more
in-depth understanding of the dynamics at play in real-world networks.
2. Based on the methods and skills
acquired during the course, the students will be able to engage in research
activities related to measuring and analyzing datasets from computer networks.
3. Several notions and ideas
presented during the course with reference to packet networks are actually much
more general, and apply mutatis mutandis
to other classes of large-scale systems in other domains.
Examination: verbal colloquium and project assignment.
- nosilec: Fabio Ricciato
Tema predmeta so sodobni algoritmi za učenje iz podatkovnih tokov. Učili se bomo o odprtih izzivih na področju (inkrementalni modeli za nadzorovano učenje, stiskanje podatkov, odkrivanje spremembe v porazdelitvi toka (concept drift), gručenje iz podatkov, specializirane statistike za vrednotenje uspešnosti). S pridobljenim znanjem bo študent sposoben uporabljati svoje znanje o strojnem učenju pri aplikacijah, ki so povezane z obilico vsakdanjih podatkov (finančne in bančne transakcije, vremenski podatki, senzorski podatki itd.).
Predmet bo organiziran kot kombinacija predavanj in laboratorijskih vaj (te bodo izvedene z uporabo statističnega paketa R). V okviru vaj bodo študenti znanje aplicirali na izbranem problemu, ki je lahko direktno povezan tudi s tematiko doktorske naloge. V preostanku semestra bo organizirano tudi medsebojno tekmovanje za izdelavo najbolj točnega napovednega modela na podanih podatkih.
- nosilec: Zoran Bosnić
- nosilec: Lovro Šubelj
Študentje se bodo pri
predmetu seznanili z različnimi nalogami napovedovanja strukturiranih izhodnih
spremenljivk in z različnimi pristopi za njihovo reševanje. Spoznali bodo nekaj
najsodobnejših orodij za reševanje te vrste nalog in jih uporabili na praktičnih
problemih. Naučili se bodo tudi uporabljati metode napovedne analize strukturiranih
podatkov v kontekstu lastnega raziskovalnega dela. Predictive Analytics for Structured Data
The course will introduce the students to
different tasks of structured output prediction and describe a variety of
approaches for solving such tasks. The students will get to know some
state-of-the-art tools for solving such tasks and examples of their use in
practice. Within the course, the students will learn to apply predictive analytics
methods for structured data in the context of their research.
The course will cover the following topics:
different tasks of structured output prediction, such as multi-target
classification/ regression and (hierarchical) multi-label classification.
clustering methods (tree and rule-based) for structured output prediction.
for data mining and their use for describing structured output prediction.
methods for structured output prediction (tree and rule ensembles).
of structured output prediction to different practical problems, from
areas such as environmental/ life sciences and image annotation/ retrieval.
- nosilec: Džeroski Sašo