BIG DATA & BUSINESS ANALYTICS

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1 ΚΕΝΤΡΟ ΣΥΝΕΧΟΥΣ ΕΚΠΑΙΔΕΥΣΗΣ & ΕΠΑΓΓΕΛΜΑΤΙΚΗΣ ΚΑΤΑΡΤΙΣΗΣ ΟΙΚΟΝΟΜΙΚΟΥ ΠΑΝΕΠΙΣΤΗΜΙΟΥ ΑΘΗΝΩΝ (Α.Σ.Ο.Ε.Ε.) ΜΑΚΡΟΧΡΟΝΙΟ ΠΡΟΓΡΑΜΜΑ ΕΞΕΙ ΙΚΕΥΣΗΣ (Specialization Program) BIG DATA & BUSINESS ANALYTICS Ακαδηµαϊκό Έτος

2 ΠΕΡΙΕΧΟΜΕΝΑ 1 ΕΙΣΑΓΩΓΙΚΑ: BIG DATA, ANALYTICS & DATA SCIENCE Big Data & Business Analytics Data Science ΣΚΟΠΟΣ ΤΟΥ ΠΡΟΓΡΑΜΜΑΤΟΣ ΠΡΟΦΙΛ ΥΠΟΨΗΦΙΩΝ & ΙΑ ΙΚΑΣΤΙΚΑ Προφίλ Υποψηφίων Στοιχεία της Αίτησης Χορήγηση Πιστοποιητικών ιάρκεια Προγράµµατος Χώρος Υλοποίησης ίδακτρα Επικοινωνία ΠΡΟΓΡΑΜΜΑ ΣΠΟΥ ΩΝ & ΠΕΡΙΓΡΑΦΕΣ ΜΑΘΗΜΑΤΩΝ ΕΙΣΗΓΗΤΕΣ & ΒΙΟΓΡΑΦΙΚΑ... 13

3 1 ΕΙΣΑΓΩΓΙΚΑ: BIG DATA, ANALYTICS & DATA SCIENCE Η χρησιµοποίηση δεδοµένων στη λήψη σωστών, έγκυρων και έγκαιρων αποφάσεων έχει αναχθεί σε «εκ των ουκ άνευ» παράγοντα επιτυχίας για τις περισσότερες σύγχρονες επιχειρήσεις και οργανισµούς. Ταυτόχρονα, τα τελευταία χρόνια, µε την ανάπτυξη νέων τεχνολογιών και εφαρµογών όπως η εξάπλωση των κοινωνικών δικτύων, η εκτεταµένη χρήση smart phones, η εγκατάσταση αισθητήρων κ.α. ο όγκος και η µορφή των δεδοµένων έχει αλλάξει δραµατικά, ενώ οι δυνατότητες ανάλυσης και επεξεργασίας αυτών είναι εντυπωσιακές. Αυτό επέφερε µεγάλες αλλαγές στο πως αντιλαµβάνονται τη λήψη αποφάσεων οι εταιρίες και οργανισµοί. Πλέον, τις περισσότερες φορές, αντί να αναπτύσσουν µοντέλα, ξεκινούν από την ανάλυση των δεδοµένων που κατέχουν, τα συνδυάζουν µε αλλά που συλλέγουν από διάφορες πηγές και τα µοντέλα προκύπτουν αυτόµατα µε τη µορφή προτύπων. Όπως διατυπώνει περιεκτικά το Wired Magazine [Αύγουστος 2008]: Η αναζήτηση της γνώσης άρχιζε µε µεγάλες θεωρίες. Πλέον ξεκινά µε τεράστιους όγκους δεδοµένων. Oι όροι Big Data, Business Analytics και Data Science βρίσκονται πλέον στο επίκεντρο των δραστηριοτήτων των ΙΤ τµηµάτων εταιριών και οργανισµών, ανεξαρτήτως µεγέθους. 1.1 Big Data & Business Analytics Ένας απλός ορισµός που θα µπορούσε να χρησιµοποιηθεί για την περιγραφή των Μεγάλων εδοµένων (Big Data) είναι ο εξής: «Managing, analyzing and mining data, no matter how large it is, what format it has and how fast is produced.» Όπως µπορεί να παρατηρήσει κανείς εύκολα, δεν αφορά το µέγεθος, τον τύπο ή το είδος των δεδοµένων, αλλά ένα σύνολο διαδικασιών που απαιτούν γνώσεις πληροφορικής, στατιστικής, µηχανικής εκµάθησης και διοίκησης επιχειρήσεων, που απλά εµπλέκουν και χρησιµοποιούν δεδοµένα. Πολλές φορές αναφέρεται στη σχετική βιβλιογραφία ότι τα Μεγάλα εδοµένα χαρακτηρίζονται από τα 3Vs: Volume, Variety, Velocity. Ο µεγάλος όγκος (Volume) δεδοµένων χαρακτηρίζει τις µοντέρνες εφαρµογές, ενώ τα δεδοµένα έρχονται σε διάφορες µορφές, όπως σχεσιακά δεδοµένα, εικόνα, ήχος, video (Variety.) Τέλος, η παραγωγή των δεδοµένων µπορεί να γίνεται σε πολύ µεγάλους ρυθµούς (π.χ. tweets, financial streams, αισθητήρες) και η εξαγωγή συµπερασµάτων να πρέπει να γίνει σε πραγµατικό χρόνο (Velocity.) Η Εικόνα 1 από τη µελέτη του The Data Warehousing Institute (TDWI) «The TDWI Best Practices Report, Big Data Analytics, 4th Quarter 2011» δίνει µία καλή περιγραφή των 3Vs. Εικόνα 1: The 3Vs, Volume, Variety, Velocity (πηγή: TDWI, 4th Quarter 2011)

4 H ανάγκη διαχείρισης των δεδοµένων σε εφαρµογές Big Data οδήγησε στην ανάπτυξη µίας νέας γενιάς συστηµάτων, µοντέλων και προγραµµατιστικών εργαλείων που ακόµα βρίσκται σε εµβρυακό στάδιο- όπως: Map Reduce, Hadoop και οικοσύστηµα αυτού, NoSQL, κ.α., τεχνολογίες που επιτρέπουν την παράλληλη επεξεργασία δεδοµένων σε µεγάλη κλίµακα και µε fault-tolerant τρόπο. Ταυτόχρονα, η αξιοποίηση αυτών των δεδοµένων για την παραγωγή analytics απαιτούν ικανότητες και γνώσεις σε ένα ευρύ πεδίο αντικειµένων, όπως στατιστική, τεχνητή νοηµοσύνη και τεχνικές µηχανικής εκµάθησης, επιχειρησιακή έρευνα, διοίκηση επιχειρήσεων, κ.α. 1.2 Data Science Τα τελευταία δύο χρόνια έχει αναδειχθεί ένας νέος ρόλος στις εταιρίες και τους οργανισµούς, µε την ονοµασία data scientist. Όπως αναφέρει το περιοδικό Economist («A special report on managing information: Data, data everywhere», Φεβρουάριος 2010): «Οι ιευθυντές Τεχνολογίας έχουν εξελιχθεί σε µία ανώτερη θέση στην εκτελεστική ιεραρχία µία εταιρίας και µία νέα ιδιότητα στελέχους έχει αναδειχθεί, ο επιστηµονικός υπεύθυνος δεδοµένων (the data scientist), ο οποίος συνδυάζει τα προσόντα ενός ικανού προγραµµατιστή, στατιστικού και αφηγητή µε σκοπό να ανακαλύψει τους σβώλους χρυσού που υπάρχουν κάτω από τεράστιους όγκους δεδοµένων. Ο Hal Varian, Chief Economist της Google, προβλέπει ότι το επάγγελµα του data scientist θα γίνει σύντοµα ένα από τα πλέον ελκυστικά. Όπως εξηγεί, τα δεδοµένα πλέον είναι ευρέως διαθέσιµα, αυτό που είναι σπάνιο είναι η ικανότητα εξαγωγής γνώσης από αυτά.» Επίσης, ο Tom Davenport σε πρόσφατο άρθρο του «Data Scientist: The Sexiest Job of the 21st Century» στο Harvard Business Review (Οκτώβριος 2012), επιχειρηµατολογεί για την αναγκαιότητα τέτοιων επαγγελµατιών, ενώ επισηµαίνει τη µεγάλη έλλειψη ανθρώπων σε αυτόν τον κλάδο. Η συµβουλευτική εταιρία McKinsey στη µελέτη της «Big Data: The next frontier for innovation, competition and productivity» (Ιούνιος 2011), προβλέπει ότι θα χρειαστούν data scientists και ότι περίπου 1,5 εκατ. managers θα πρέπει να εκπαιδευτούν στη χρήση και ανάλυση δεδοµένων. Μία πιο πρόσφατη µελέτη της Gartner (Οκτώβριος 2012), είναι ακόµα πιο αισιόδοξη: 1,9 εκατ. θέσεις εργασίας για data scientists και άλλα 4 εκατ. για την υποστήριξη αυτών µόνο στις Η.Π.Α. Yπάρχει µεγάλη συζήτηση για το ποιες είναι οι ικανότητες και γνώσεις που συνθέτουν το υπόβαθρο του data scientist. Καταρχάς θα πρέπει να έχει πολύ καλή, βαθειά γνώση των θεµάτων που αφορούν τη διαχείριση δεδοµένων: µοντελοποίηση δεδοµένων, γλώσσες ερωτηµάτων/ανάκτησης δεδοµένων, επεξργασία, αποθήκευση, εννοποίηση. Θα πρέπει επίσης να έχει πολύ καλό µαθηµατικό υπόβαθρο ώστε να µπορεί να κατανοήσει/χειριστεί τις στατιστικές µεθόδους και τις τεχνικές machine learning που απαιτούνται στην εξαγωγή προτύπων. Τέλος, θα πρέπει να είναι σχετικά ανήσυχο πνεύµα και να γνωρίζει κάποιες αρχές σύγχρονου management, ώστε να ακολουθεί τις εξελίξεις του σηµερινού τεχνολογικού κόσµου και να έχει µία αντίληψη πως µπορούν να χρησιµοποιηθούν τα δεδοµένα σε ένα εταιρικό περιβάλλον. Η Εικόνα 2 συνοψίζει τους βασικούς τοµείς που συνεισφέρουν στις γνώσεις ενός data scientist. Εικόνα 2: Βασικοί τοµείς γνώσεων Data Science

5 Αυτή τη στιγµή, τα πανεπιστήµια διεθνώς που προσφέρουν εξειδικευµένα προγράµµατα στον τοµέα των Big Data & Analytics είναι ελάχιστα. Τα πρώτα εξειδικευµένα πρόγραµµατα δηµιουργήθηκαν στα πανεπιστήµια Northwestern και Carnegie Mellon το Σεπτέµβριο του Πολύ σοβαρές προσπάθειες είναι η δηµιουργία του Institute of Data Sciences and Engineering στο Columbia University ( και του Data Science Center στο New York University ( Και στις δύο περιπτώσεις, σε αυτά τα Ινστιτούτα συµµετέχουν τµήµατα όλου του πανεπιστηµίου. Στο τµήµα ιοικητικής Επιστήµης και Τεχνολογίας του Οικονοµικού Πανεπιστηµίου Αθηνών, αναγνωρίζοντας νωρίς την αξία των Big Data και των Business Analytics, αλλά και τη συνάφεια µε το αντικείµενο του τµήµατός µας, εκπονήσαµε πριν δύο χρόνια µία µελέτη σκοπιµότητας ενός προγράµµατος εξειδίκευσης/µεταπτυχιακού σε αυτό το θέµα, διερευνώντας και συλλέγοντας απόψεις από καθηγητές σε µεγάλα πανεπιστήµια της Αµερικής. Το παρόν πρόγραµµα είναι το αποτέλεσµα αυτής της προσπάθειας.

6 2 ΣΚΟΠΟΣ ΤΟΥ ΠΡΟΓΡΑΜΜΑΤΟΣ Ο σκοπός του προγράµµατος είναι να προετοιµάσει στελέχη της επιστήµης των δεδοµένων (data scientists), τα οποία να µπορούν να συνδυάζουν µε τον καλύτερο τρόπο διοικητικές γνώσεις, τεχνικές διαχείρισης δεδοµένων και αναλυτικά εργαλεία, βασισµένα κυρίως στη στατιστική και στην µηχανική εκµάθηση, µε σκοπό τη βέλτιστη λήψη επιχειρηµατικών αποφάσεων. ηλαδή να µπορούν να χειριστούν τις πληροφορίες ενός γρήγορα και συνεχώς µεταβαλλόµενου κόσµου, πλούσιου σε δεδοµένα, µε έναν τρόπο ευέλικτο, αποδοτικό και αποτελεσµατικό για το περιβάλλον στο οποίο δραστηριοποιούνται. Για να επιτευχθεί αυτό θα πρέπει να αποκτήσουν γνώσεις και εµπειρία σε τέσσερεις βασικούς τοµείς: (α) διαχείριση δεδοµένων, (β) στατιστική και µαθηµατικά λήψης αποφάσεων, (γ) διοικητική επιστήµη και (γ) εργαλεία και συστήµατα παραγωγής analytics.

7 3 ΠΡΟΦΙΛ ΥΠΟΨΗΦΙΩΝ & ΙΑ ΙΚΑΣΤΙΚΑ 3.1 Προφίλ Υποψηφίων Το προτεινόµενο υπόβαθρο των υποψηφίων είναι Τεχνολογικό (πληροφορική, µηχανικοί), Θετικές Επιστήµες (µαθηµατικά, φυσική), ιοικητική Επιστήµη, αλλά οποιοδήποτε υπόβαθρο θα ληφθεί υπόψη, ανάλογα και µε τις επιδόσεις του υποψηφίου. Η προϋπηρεσία δεν είναι απαραίτητη, αλλά επιθυµητή. Το ακόλουθο σχόλιο για το προφίλ των υποψηφιών ανήκει στον καθηγητή του Stern Business School του πανεπιστηµίου της Νέας Υόρκης (NYU) και ειδικού της Google για anti-fraud στο Google Adwords κ. Alex Tuzhilin, ο οποίος βοήθησε στη διαµόρφωση του παρόντος προγράµµατος: «Η ελάχιστη απαίτηση: οι φοιτητές να γνωρίζουν κάτι από τεχνολογία και business (µε τον όρο τεχνολογία, εννοώ να έχουν υπόβαθρο πληροφορικής ή µηχανικού.) Επίτηδες έχω αφήσει το «κάτι» να είναι ασαφές. Η χειρότερη περίπτωση είναι κάποιος που γνωρίζει αρκετά από business και λίγα από τεχνολογία. Τότε θα απαιτηθούν διορθωτικές ενέργειες, που γενικά είναι δύσκολο. Εξ ορισµού, ο φοιτητικός πληθυσµός σας θα είναι bi-modal και η πρόκληση για το πρόγραµµά σας θα είναι να καταφέρει να συνυπάρξουν. Το πλεονέκτηµα είναι ότι µπορείτε να τους κάνετε να µάθουν ο ένας από τον άλλον κατά τη διάρκεια των projects και µπορείτε να χρησιµοποιήσετε αυτό το diversity card σε διάφορα άλλα σηµεία. Ίσως θα «έσπρωχνα» αυτό το business/technology tradeoff προς την πλευρά της τεχνολογίας, καθώς κάποια µαθήµατα µπορεί να είναι αρκετά τεχνικά και κάποιοι φοιτητές κατεύθυνσης business µε περιορισµένη γνώση τεχνολογίας µπορεί να αντιµετωπίσουν δυσκολίες.» Το πρόγραµµα θα δεχτεί µέχρι 35 συµµετέχοντες για το ακαδηµαϊκό έτος Στοιχεία της Αίτησης H αίτηση µπορεί να παραληφθεί ηλεκτρονικά στον ιστότοπο και να αποσταλεί µε . Η αίτηση περιλαµβάνει προσωπικά στοιχεία, τίτλους και πληροφορίες σπουδών, βαθµολογίες πτυχίων, απόδειξη γνώσης της αγγλικής γλώσσας, περιγραφή προϋπηρεσίας (αν υπάρχει) και τα στοιχεία δύο ατόµων που θα αποστείλουν συστατικές επιστολές για τον αιτούντα/την αιτούσα. 3.3 Χορήγηση Πιστοποιητικών Στους επιτυγχόντες σε όλα τα εξεταζόµενα µαθήµατα, το KEK του Οικονοµικού Πανεπιστηµίου Αθηνών χορηγεί Πιστοποιητικό Επαγγελµατικής Εκπαίδευσης και Κατάρτισης, µε Απόφαση της Συγκλήτου του ΟΠΑ (στις 13/01/2000), στην οποία αναγράφεται ο µέσος όρος της βαθµολογίας όλων των εξεταζόµενων µαθηµάτων και η αντίστοιχη επίδοση. Οι Βαθµοί από 60 έως 75 αποτελούν την κατηγορία «Καλώς», από 76 έως 90 «Λίαν Καλώς» και από «Άριστα». 3.4 ιάρκεια Προγράµµατος Χώρος Υλοποίησης

8 Η διάρκεια του προγράµµατος είναι 250 ώρες. Τα µαθήµατα θα γίνονται στις αίθουσες του KEK του Οικονοµικού Πανεπιστηµίου Αθηνών (οδός Κεφαλληνίας 46 και Πατησίων, 6ος όροφος). Ηµέρες και ώρες µαθηµάτων: ευτέρα και Τετάρτη, 18:00 21:00. Τα µαθήµατα θα αρχίσουν τον Οκτώβριο του 2013 και θα ολοκληρωθούν τον Ιούνιο του Oι ακριβείς ηµεροµηνίες θα ανακοινωθούν στο ίδακτρα Τα δίδακτρα του προγράµµατος είναι δύο χιλιάδες επτακόσια ευρώ (2.700,00 ) και θα πρέπει να καταβληθούν σε 3 δόσεις, κατά τη διάρκεια του ακαδηµαϊκού έτους Υπάρχει δυνατότητα επιστροφής του συνολικού κόστους του προγράµµατος ή µέρους αυτού στις επιχειρήσεις, µέσω της εργοδοτικής εισφοράς 0,45% του ΟΑΕ (πρόγραµµα ΛΑΕΚ). 3.6 Επικοινωνία Κ.Ε.Κ. Οικονοµικού Πανεπιστήµιου Αθηνών, Κεφαλληνίας 46 & Πατησίων, Αθήνα, Πέτρος Καλκάνης (petros@rc.aueb.gr), Νεκταρία Λυκούρη, Ροδίτη Ράνια Τηλ. : , -371, -372

9 4 ΠΡΟΓΡΑΜΜΑ ΣΠΟΥ ΩΝ & ΠΕΡΙΓΡΑΦΕΣ ΜΑΘΗΜΑΤΩΝ Το πρόγραµµα σπουδών εµφανίζεται συνοπτικά στον Πίνακα 1. Τίτλος Ώρες Εισηγητής Modern Data Management 15 Αναπ. Καθ. Βασίλης Βασσάλος Business Intelligence 15 Επικ. Καθ. αµιανός Χατζηαντωνίου Statistics for Analytics I 15 Αναπ. Καθ. ηµήτρης Καρλής Computational Intelligence 15 Καθ. Χρήστος Ταραντίλης ΒΑΣΙΚΑ ΜΑΘΗΜΑΤΑ Statistics for Analytics II 15 Αναπ. Καθ. Παναγιώτης Τσιαµυρτζής Business Process Management 12 Αναπ. Καθ. Αγγελική Πουλυµενάκου Mining Big Datasets 15 Αναπ. Καθ. Γιάννης Κωτίδης Big Data Systems 15 Επικ. Καθ. αµιανός Χατζηαντωνίου Engineering Big Data Systems 12 Καθ. ιοµήδης Σπινέλλης Enterprise Resource Planning 12 Καθ. Γιώργος Ιωάννου Innovation & Entrepreneurship 12 Επικ. Καθ. Κατερίνα Πραµατάρη Text Analytics and Social Media Analytics 12 ρ. Χάρης Παπαγεωργίου Social Network Analysis 12 Καθ. Γιώργος Γιαγλής Privacy and Personal Data Protection 6 Επικ. Καθ. Λίλιαν Μήτρου Data Visualization 3 ρ. Ανδρέας Μανιάτης, CyberStream Healthcare Analytics 3 <Θα ανακοινωθεί> Public Sector Analytics 3 <Θα ανακοινωθεί> ΕΙ ΙΚΑ ΘΕΜΑΤΑ Energy Management Analytics 3 ρ. Βασίλης Νικολόπουλος, Intelen Learning Analytics 3 <Θα ανακοινωθεί> Transportation Analytics 3 <Θα ανακοινωθεί> Telecom Analytics 3 <Θα ανακοινωθεί> Marketing Analytics 3 ρ. ηµήτρης Καµπράνης, IBM Human Resource Analytics 3 <Θα ανακοινωθεί> Behaviour Analytics 3 ρ. Χάρης Παπαγεωργίου Hadoop/Amazon EC2 6 R 6 ΕΡΓΑΣΤΗΡΙΑ Mongo/Couch 6 Memcached/Riak/Redis 3 Neo4j 3 IBM 6 Oracle 3 SAS 12 Capstone Project / Thesis Πίνακας 1: Συνοπτικό Πρόγραµµα Σπουδών

10 Ακολουθεί αναλυτική περιγραφή των βασικών µαθηµάτων. Modern Data Management This will be a crash course on the important developments in data management in the last 20 years. As such, the course will focus on the basics of distributed data management, including design of distributed databases, distributed processing and optimization, and different levels of consistency and concurrency control. We will also consider the two main data integration approaches, virtual data integration and data warehousing. Finally, recent architectural innovations for increased performance, specifically columnar data placement and fully memorybased data management, will be discussed. The knowledge acquired in the course will allow a participant to evaluate competing techniques and technologies for large scale data management and also to focus on the important issues and tradeoffs when designing for performance, scalability and correctness. Business Intelligence Data warehousing, decision support, OLAP and data mining, what many people collectively call Business Intelligence (BI), has reached a maturity height with abundance of systems, platforms and methods. It has evolved from a niche area for large and highly sophisticated corporations to an essential component of any modern business entity or institution. This course will review basic BI concepts, such as: design, implementing and modeling of data warehouses, star schemas, data cubes, OLAP, tools & systems and design methodologies. It will also cover new trends in BI such as main-memory BI and column-oriented systems. Statistics for Analytics I Topics will include: introduction to Probability (intro laws Bayes theorem independence); introduction to statistics (random variables moments some discrete/continuous distributions univariate/multivariate); inferential statistics (point/interval estimation & hypothesis testing); regression (simple multiple logistic); quality control (Magnificent 7 Pareto charts control charts CUSUM EWMA); bayesian statistics decision theory (loss function Bayes optimality minimax rules). Computational Intelligence This course introduces advanced optimization tools and techniques with the main emphasis being on the application of computational intelligence algorithms to different problems and cases which arise in business and industry, such as transportation, logistics, production and services. On completion of this course, students should be able to: broaden their exposure to computational methodologies; analyze and design effective computational intelligence algorithms for complex business problems, and; provide examples and cases of how the computational intelligence algorithms can be used to solve real-life problems. The course material includes the following thematic areas: construction and local search algorithms; simulated annealing algorithms; tabu search algorithms; ant colony optimization; evolutionary algorithms. Statistics for Analytics II Topics will include: sampling; data reduction (PCA and factor analysis); clustering methods (hierarchical, partition methods, K-means and other algorithms); classification methods (discriminant, desicion trees, kernel based methods, other methods); predictive analytics. Business Process Management The course introduces the principles and techniques for the analysis of business processes from the organisational (structure), functional (activities), informational (data and systems) and control (business events and rules)perspectives. Further, two fundamental aspects of business

11 process management are detailed: structural and performance evaluation and revision. Examples are given in the context of the SAP ERP enterprise IS platform. Mining Big Datasets Understanding of big data can help improve decision making in big enterprises. Existing techniques are dwarfed by the complexity, variety, scale and dynamics of big data. In this course we will first identify the major challenges in mining big datasets in modern applications of interest. We will then overview emerging computational platforms in the area of large-scale distributed processing and discuss recent algorithmic results that can help attack big data mining problems. Big Data Systems The enormous size of today s data sets and the specific requirements of modern applications, necessitated the growth of a new generation of data management systems, where the emphasis is put on distributed and fault-tolerant processing. New programming paradigms have evolved, novel systems and tools have been developed and an abundance of startups offering?? solutions appeared. This course will be based on MapReduce and NoSQL systems. Topics include: MapReduce programming, Hadoop, Pig and Hive, developing applications in Amazon s EC2 environment, key-value stores such as Memcached and Redis, document stores such as Mongo and CouchBase and graph databases such as Neo4j. Engineering Big Data Systems Engineering software that can efficiently handle large data sets requires specialized skills and familiarity with sophisticated tools. The course will start with an overview of general purpose tools and then describe how cloud infrastructures can be configured and used for large data processing. Then a systematic method for locating and addressing performance issues will be presented. For the cases where specialized processing is required, we will examine low-level techniques, like memory mapping and copy-on-write. Finally, we will see how visualization of big data can be performed and automated. Enterprise Resource Planning Enterprise Resource Planning (ERP) Systems are the backbone software applications that enable most operational processes of today s complex organizations. This course offers students all the background necessary to address selection, implementation and advanced use of ERPs. Specifically, during the course students will understand what exactly ERPs are, their technological architecture, the business functions they address, and the critical success factors for ERP implementations. Demos of standard ERP functionality and case studies illustrating ERP implementations from Greece and abroad, support the course concepts. Innovation & Entrepreneurship The growth of electronic channels over the last decade paired with developments in social media, Web 2.0 and crowd sourcing, sensor networks and ubiquitous computing has led to an explosion of data. Due to the speed of developments, most of these data remain unexploited and the need to derive meaningful information and knowledge out of them has increased to an unprecedented degree. This fact has created a new landscape for innovation and entrepreneurship, opening up new opportunities for the development of new tools, services and offerings that respond to this need. The objective of this course is to provide the theoretical and practical basis that will allow students to identify business opportunities and innovation areas associated with the exploitation of big data and design innovative services in response to the identified business needs. Moreover, the course will provide guidelines in the area of business planning to support an entrepreneurial mindset. A series of case studies will be discussed under this perspective, while students will have the opportunity to propose their own ideas exploiting big data analytics, evaluate alternative business models and practically develop the respective business plans.

12 Text Analytics and Social Media Analytics The massive availability of user-generated content (e.g., forums, blogs, social media) has opened a new market for innovative services. Businesses are demanding more and more information with the hope of gaining more and more insights allowing them to make better business decisions. This course focuses on the content analysis of vast amounts of data streams. We will study the basic concepts and methodologies and get familiar with effective machine learning techniques and best practices of how to set up, organise and perform analytics tasks. A brief overview of the course content: topics in machine learning and systems design; entity extraction and linking with KBs; opinion mining and sentiment analysis; knowledge graphs and graph databases; social media and multimedia analysis; search engines and information retrieval. Social Network Analysis The aim of the course is to introduce students to social network analytics (SNA) and their instrumental value for businesses and the society. SNA encompasses techniques and methods for analyzing the constant flow of information over online social networks (e.g. Facebook posts, twitter feeds, foursquare check-ins) aiming to identify, sometimes even in real-time, patterns of information propagation that are of interest to the analyst. The course will provide students with an in-depth understanding of the opportunities, challenges and threats arising by online social media as far as businesses and the society at large are concerned. It will use case-based teaching and discussions to introduce students to the social and ethical issues that often arise by mining the publicly available information across online social networks for business purposes and/or other types of analyses. Finally, students will be introduced to the concepts of the wisdom of the crowds and social learning, investigating the conditions under which opinion convergence (asymptotic learning) or herding may occur in online social networks. Privacy and Personal Data Protection Definition of Privacy and personal data protection. Basic principles, institutions and regulatory models of privacy and data protection on international and national level. European and Greek regulatory framework of personal data protection. Privacy and Data Protection preserving data mining. Privacy, data protection and profiling. Issues of privacy and personal data protection in relation to Big Data (categorization of data, purpose limitation principle, consent etc.) Capstone Project A 3-month internship in the industry or a thesis writing.

13 5 ΕΙΣΗΓΗΤΕΣ & ΒΙΟΓΡΑΦΙΚΑ Οι διδάσκοντες στο πρόγραµµα εξειδίκευσης «Big Data & Business Analytics» είναι κυρίως καθηγητές του Οικονοµικού Πανεπιστηµίου Αθηνών, µε µεγάλη εµπειρία στο χώρο τους, πλούσια ερευνητική δραστηριότητα και κάτοχοι διδακτορικού διπλώµατος από κορυφαία πανεπιστήµια διεθνώς, όπως: Columbia University, Imperial University, London School of Economics, Stanford University, University of Frankfurt, University of Maryland, University of Minnesota. Αναπ. Καθ. Βασίλης Βασσάλος, Τµήµα Πληροφορικής, Ο.Π.Α. Vasilis Vassalos is an Associate Professor at the Department of Informatics of the Athens University of Economics and Business. Prior to that, he was an Assistant Professor at the Department of Information Systems in the Stern School of Business of New York University ( ), and at AUEB ( ). He graduated from Stanford University with a Ph.D. in Computer Science in He also has an M.Sc. from Stanford University in Computer Science (1998) and an Electrical Engineering Diploma from NTUA, graduating first in his class. He has published more than 45 research papers in international peer-reviewed journals and conferences in the areas of databases and the Web, such as ACM SIGMOD, ACM TODS, VLDB Conference, IEEE ICDE Conference, EDBT Conference, IEEE TKDE, WWW Conference, and more. He holds 2 US patents for work on information integration, and regularly serves on the program committees of the major conferences in databases, and as a reviewer for the major journals. He was PC Co-Chair of the 14th International Workshop on the Web and Databases (WebDB 2011). He was a co-founder of a startup company in the space of Enterprise Information Integration (Enosys Software, founded in 2000, acquired by BEA Systems in 2003). Ηe was a Visiting Professor at UCSD and a Marie Curie Outgoing International Fellow in and a Visiting Professor at EPFL in Spring Καθ. Γιώργος Γιαγλής, Τµήµα ιοικητικής Επιστήµης & Τεχνολογίας, Ο.Π.Α. George is Vice Rector of Finance & Development and Professor of ebusiness at the Athens University of Economics and Business, Greece. He has previously worked with the University of the Aegean (Greece) and Brunel University (UK), while he has held visiting posts in universities in the UK, Australia, USA, Finland and Denmark. In 2001, George founded the ISTLab Wireless Research Center, the first research center in Greece with a focus on mobile business, applications and services, while since 2009 he is the Director of Sociomine, a newly-founded research center with a focus on Social Network Analytics. He has also been elected as academic representative in the Coordinating Committee of the Hellenic Mobile Cluster. George has published more than 150 articles in leading journals and international conferences and has authored ten books with Greek and international publishers. His scientific contribution has been acknowledged by the international academic community, as evidenced by the large number of citations (more than 3,000 citations) and the best paper and teaching awards he has received. He serves at the Editorial Board of seven international academic journals and has served at the organizing committees of more than 40 international conferences. From 2003 to 2008, he was Permanent Secretary of the International Conference on Mobile Business, which he organized in Athens in His research and teaching interests focus on a) electronic business, emphasizing on the design, development and evaluation of innovative mobile, social networking and business applications, b) simulation modeling, business process modeling and system dynamics, c) social

14 network analytics, focusing on data mining, user modeling and social learning behavior in online social networks and d) ubiquitous and pervasive information systems. Καθ. Γιώργος Ιωάννου, Τµήµα ιοικητικής Επιστήµης & Τεχνολογίας, Ο.Π.Α. Dr. George Ioannou is Professor of Production & Operations Management at the Athens University of Economics and Business. He serves as the Director of the MBA International Program, and as the Head of the Operations & ERP Systems Center within the Management Science Laboratory. He was an Assistant Professor at the Department of Industrial and Systems Engineering of Virginia Tech, directing the Manufacturing Systems Integration Laboratory. Dr. Ioannou received his diploma in Mechanical Engineering from the National Technical University of Athens, and his M.Sc./DIC in Industrial Robotics and Manufacturing Automation from Imperial College, London, UK. He was a Graduate Research Assistant at the Institute for Systems Research of the University of Maryland at College Park, USA, where he received his Ph.D. in Mechanical Engineering. His research concentrates on the quantitative and analytical study of business systems, and merges operations research tools with modern information technology to address open problems faced by today s complex enterprises and supply chain networks. His work has been sponsored by several research organizations and private companies from the US, Europe and Greece (Toshiba, Motorola, Siemens, Cyclon, Avin, SAS Institute, NSF, European Commission, General Secretariat for Research and Technology, Ministry of Development, Ministry of Education, etc.), while his publications have appeared in various archival journals and cover topics ranging from facility and material handling system design and operation, to Enterprise Resource Planning Systems. He has consulted for many companies and public organizations, and was responsible for executive and educational seminars in his areas of expertise, both in the US and Greece. He has been honored by many Teaching Excellence Awards for his MBA courses, has been recognized by the Board of the Athens Chamber of Industry and Commerce, and he is the recipient of the Microsoft Excellence in Education Award. Αναπ. Καθ. ηµήτρης Καρλής, Τµήµα Στατιστικής, Ο.Π.Α. Dimitris Karlis is Associate Professor at the Department of Statistics, Athens University of Economics and Business (AUEB). He received a BSc. in Statistics from Department of Statistics, AUEB in 1992 and a PhD in Statistics from the same department in He has published approximately 70 papers in peer reviewed statistical journals. His research interest refer to mixture models, computational statistics and especially stochastic algorithms, multivariate count data analysis, models for statistical analysis for sports data and modeling dependent data via copulas. He is Associate editor of Metron journal, Communications in Statistics (both Theory and Methods and Computation and Simulation), IMA Journal of Management Mathematics and Stochastic Environmental Research and Risk Assessment, while he has acted as referee for more than 135 papers. He is also editor of Biometrics Bulletin of IBS. He has supervised 2 PhD student (currently the one is lecturer in Univ. of East Anglia, UK and the other Post-Doc in University of Cyprus), 14 Master thesis, while at this moment he supervises two PhD students. He has been invited in several conferences around the world. He is member of the American Statistical Society, elected member of the International Statistical Institute, member of the International Association of Statistical Computing, publicity officer of the Eastern Mediterranean Region of the International Biometrics Society and member of the Greek Statistical institute. He has also participated in several European projects related to statistics and mainly to official statistics. Αναπ. Καθ. Γιάννης Κωτίδης, Τµήµα Πληροφορικής, Ο.Π.Α. Dr. Yannis Kotidis is an Associate Professor in the Department of Informatics at the Athens University of Economics and Business. He holds a B.Sc. degree in Electrical Engineering and Computer Science from the National Technical University of Athens, an M.Sc. and a Ph.D. in Computer Science from the University of Maryland (USA). Between 2000 and 2006 he was a Senior Technical Specialist at the Database Research Department of AT&T Labs-Research in

15 Florham Park, New Jersey. His main research areas include large scale data management systems, data warehousing and data mining. He has published more than 80 articles in international conferences and journals and holds 5 U.S patents. He has served on numerous organizing and program committees of international conferences related to data management. Επικ. Καθ. Λίλιαν Μήτρου, Τµήµα Μηχαν. Πληρ. & Επικ. Συστηµάτων, Πανεπ. Αιγαίου Dr. Lilian Mitrou is Associate Professor at the University of the Aegean-Greece (Department of Information and Communication Systems Engineering) and Visiting Assistant Professor at the Athens University of Economics. She teaches information law and data protection law. L. Mitrou holds a PhD in Data Protection (University of Frankfurt-Germany). She has served as a Member of the Hellenic Data Protection Authority ( ). She has also served as Advisor to the former Prime Minister K. Simitis in sectors of Information Society and Public Administration ( ). She served and still serves as member of many Committees working on law proposals in the fields of privacy and data protection, communications law, e-government etc. Her professional experience includes senior consulting and researcher positions in a number of private and public institutions on national and international level. Her research interests include: Privacy and Data Protection, e-democracy and egovernment services, Internet Law. L. Mitrou published books and chapters in books (in Greek, German and English) and many journal and conference papers. ρ. Χάρης Παπαγεωργίου, Ινστιτούτο Επεξεργασίας Λόγου & Ερευνητικό Κέντρο ΑΘΗΝΑ Haris Papageorgiou is a senior researcher at the Institute for Language and Speech Processing (ILSP/R.C. Athena ) and co-founder of Qualia, a business intelligence studio established in Haris is responsible for building advanced multimedia analytics for scalable data systems. He has held Chief Scientist positions in several european and national projects in the area of multilingual multimodal multimedia processing. Haris holds an M.Sc. and Ph.D. in Computer Science from NTUA and a B.Sc. in Electrical Engineering from NTUA. He has published more than 50 papers in international scientific books, journals and international conferences. He holds a patent in Machine Translation technology. Αναπ. Καθ. Αγγελική Πουλυµενάκου, Τµήµα ιοικητικής Επιστήµης & Τεχνολογίας, Ο.Π.Α. Angeliki Poulymenakou is Associate Professor in the Department of Management Science & Technology of the Athens University of Economics and Business. Prior to this she has served as Lecturer in Information Systems at the Information Systems Department of the London School of Economics and Political Science. She holds a first degree in Mathematics (University of Athens), and MSc and PhD degrees in Information Systems both from the London School of Economics and Political Science. Her research interests over the years have addressed advanced knowledge analysis and management methods (PhD), inter-organisational networks, IT enabled organisational change and new methods of work, business processes level implications of ERP, e-learning particularly in workplace contexts, e-government (studies in Brazil and Greece), and implications of digital entrepreneurship (China). She has published 25 papers in international journals, 50 papers in peer reviewed international conferences and two books (by Springer Berlin and Kluwer Academic Publishers). Currently she serves on the editorial board of three international journals, while she has served on the program committee and as associate editor for international information systems conferences for multiple years (ICIS, ECIS, IFIP). She has been program co-chair and organising chair for the IFIP WG 8.2 & 9.4 conference (Athens, 2003), program chair for MCIS (Athens, 2009) and programme and organising chair for IFIP WG 9.5 (Athens, 2009). She has acted as research coordinator in 20 EU and national funded Research and Development projects most of them involving collaboration with academic and industrial partners across Europe. Since 2001 she is the Director of the Organisational Information Systems Research Group first in the ELTRUN and then in the ISTLab of the Department of Management Science and Technology of the AUEB.

16 Επικ. Καθ. Κατερίνα Πραµατάρη, Τµήµα ιοικητικής Επιστήµης & Τεχνολογίας, Ο.Π.Α. Katerina Pramatari is Assistant Professor at the Department of Management Science and Technology of the Athens University of Economics and Business (AUEB) and scientific coordinator of the ELTRUN-SCORE (Supply Chain & Demand Management Collaboration and Electronic Services) research group. She holds a B.Sc. in Informatics and M.Sc. in Information Systems from AUEB, and a Ph.D. in Information Systems and Supply Chain Management also from AUEB. She has worked as a systems analyst for Procter & Gamble European Headquarters and in the Marketing Department of Procter & Gamble Greece, as well as in the setup of B2B ventures. She has received several business and academic distinctions and scholarships. Her research and teaching areas are supply chain information systems, e-business integration and electronic services. During the last six years she has been supporting the e-nnovation student competition on digital innovation and entrepreneurship. She has published more than 90 papers in scientific journals, peer-reviewed academic conferences and book chapters. Amongst others she has published in the Journal of Retailing, European Journal of Information Systems, Information Systems Journal, Decision Support Systems, Journal of Strategic Information Systems, The European Journal of O.R., Computers and O.R., Supply Chain Management-An International Journal, Journal of Information Technology. Καθ. ιοµήδης Σπινέλλης, Τµήµα ιοικητικής Επιστήµης & Τεχνολογίας, Ο.Π.Α. Diomidis Spinellis is a Professor in the Department of Management Science and Technology at the Athens University of Economics and Business, Greece. From 2009 to 2011 he instituted and delivered a demanding turnaround process, serving as the Secretary General for Information Systems at the Greek Ministry of Finance. His research interests include software engineering, IT security, and programming languages. He has written two award-winning, widely-translated books: Code Reading and Code Quality: The Open Source Perspective. Dr. Spinellis has also published more than 200 technical papers in journals and refereed conference proceedings, which have received more than 2000 citations. He is a member of the IEEE Software editorial board, authoring the regular Tools of the Trade column. He has contributed code that ships with Mac OS X and BSD Unix and is the developer of UMLGraph and other open-source software packages, libraries, and tools. He holds an MEng in Software Engineering and a PhD in Computer Science, both from Imperial College London. Dr. Spinellis serves as an elected member of the IEEE Computer Society Board of Governors ( ), and is a senior member of the ACM and the IEEE. Καθ. Χρήστος Ταραντίλης, Τµήµα ιοικητικής Επιστήµης & Τεχνολογίας, Ο.Π.Α. Christos D. Tarantilis is Professor and Head of the Department of Management Science & Technology of the Athens University of Economics and Business as well as the Director of the ISO-certified Management Science Laboratory (MSL) of the University. He mainly works on the design, development and application of mathematical models, operations research techniques and computationally efficient algorithms to enable the use of Decision Support Systems. He has more than 120 scientific papers in international academic journals (INFORMS, Willey, IEEE, Elsevier), books and conferences, including more than 50 journal papers in Web of Scienceindexed journals. Some of his work is used as instruction material in academic programs of N. America and Europe, while his algorithms for the solution of large-scale problems in transportation and logistics have been internationally acclaimed. In addition, Prof. Tarantilis has been sixteen (16) times the recipient of the "Best Teaching Faculty Award" of his undergraduate and postgraduate/mba courses. Αναπ. Καθ. Παναγιώτης Τσιαµυρτζής, Τµήµα Στατιστικής, Ο.Π.Α. Panagiotis Tsiamyrtzis received the BS degree in Mathematics from the Aristotle University of Thessaloniki, Greece. He obtained the MS and PhD degrees in Statistics from the University of Minnesota, U.S.A., where he worked as visiting faculty. In 2004, he joined the Department of Statistics at the Athens University of Economics and Business, where he is currently Assistant

17 Professor. His research interests include Bayesian statistical process control (quality control), statistical aspects of computer vision problems and applications of Bayesian statistics. He is the author of more than forty refereed research papers and recipient of best paper awards in two conferences (American Statistical Association & European Network for Business and Industrial Statistics). As of 2011, he is also a Research Assistant Professor at the Department of Computer Science, University of Houston, TX, U.S.A. Επικ. Καθ. αµιανός Χατζηαντωνίου, Τµήµα ιοικητικής Επιστήµης & Τεχνολογίας, Ο.Π.Α. (Επιστηµονικός Υπεύθυνος του Προγράµµατος) Damianos Chatziantoniou received his B.Sc. in Applied Mathematics from the University of Athens (June 1991, summa cum laude) and continued his studies in Computer Science at Courant Institute of Mathematical Sciences of New York University (M.Sc., Dec. 1992) and Columbia University (M.Phil., Feb. 1996, Ph.D., June 1997). His academic research interests include big data, business intelligence (data warehousing, OLAP), large-scale analytics (MapReduce and beyond), query processing, data streams and real-time analysis. He has published more than 30 articles at top conferences and journals, such as VLDB, ICDE, EDBT, KDD, SIGMOD, Journal of Information Systems, Journal of Data and Knowledge Engineering and elsewhere. His research work has influenced Microsoft s SQL Server (query processor), Oracle s 8i and 9i Systems (Analytic Functions for OLAP, a benchmark for any BI system), and ANSI SQL Standard (OLAP Amendment). There are more than 11 U.S. patents using Damianos research work as primary reference (among these, 4 from Microsoft, 2 from IBM and 2 from Oracle) and more than 150 citations to his work, including 9 data management textbooks. He is currently an Assistant Professor at Athens University of Economics and Business (AUEB) - Department of Management Science and Technology. Prior to AUEB, he has served as a tenuretrack Assistant Professor at Stevens Institute of Technology (September 1997-December 1999.) Besides academia, Damianos has been involved in several technology start-up companies. Panakea Software Inc. (founder, 1998), based in New York City, developed and marketed database querying/reporting technology to make certain analytic tasks (OLAP) easier to express and faster to evaluate. Clients included Dun & Bradstreet, Columbia-Presbyterian Medical Center and Philips North America. VoiceWeb SA (founder, 2001), based in Athens, focused on speech & telecom applications. In 2011, Bank of Piraeus acquired a stake at Voiceweb. Clients include Vodafone, Wind, Alpha Bank, Village Cinemas, Athens Festival, etc. Damianos has served in as a senior research consultant in Aster Data Systems, a database technology based on Silicon Valley, dealing with big data. Aster Data was acquired in March of 2011 by Teradata.