BIG DATA & BUSINESS ANALYTICS

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1 ΚΕΝΣΡΟ ΤΝΕΧΟΤ ΕΚΠΑΙΔΕΤΗ & ΕΠΑΓΓΕΛΜΑΣΙΚΗ ΚΑΣΑΡΣΙΗ ΟΙΚΟΝΟΜΙΚΟΤ ΠΑΝΕΠΙΣΗΜΙΟΤ ΑΘΗΝΩΝ (Α..Ο.Ε.Ε.) ΜΑΚΡΟΥΡΟΝΙΟ ΠΡΟΓΡΑΜΜΑ ΔΞΔΙΓΙΚΔΤΗ (Specialization Program) BIG DATA & BUSINESS ANALYTICS Ακαδημαϊκό Έηορ

2 ΠΔΡΙΔΥΟΜΔΝΑ 1 ΔΙΑΓΩΓΙΚΑ: BIG DATA, ANALYTICS & DATA SCIENCE Big Data & Business Analytics Data Science ΚΟΠΟ ΣΟΤ ΠΡΟΓΡΑΜΜΑΣΟ ΠΡΟΦΙΛ ΤΠΟΨΗΦΙΩΝ & ΓΙΑΓΙΚΑΣΙΚΑ Πξνθίι Τπνςεθίσλ ηνηρεία ηεο Αίηεζεο Υνξήγεζε Πηζηνπνηεηηθψλ Γηάξθεηα Πξνγξάκκαηνο Υψξνο Τινπνίεζεο Γίδαθηξα Δπηθνηλσλία ΠΡΟΓΡΑΜΜΑ ΠΟΤΓΩΝ & ΠΔΡΙΓΡΑΦΔ ΜΑΘΗΜΑΣΩΝ ΔΙΗΓΗΣΔ & ΒΙΟΓΡΑΦΙΚΑ... 13

3 1 ΔΙΑΓΧΓΙΚΑ: BIG DATA, ANALYTICS & DATA SCIENCE Η ρξεζηκνπνίεζε δεδνκέλσλ ζηε ιήςε ζσζηψλ, έγθπξσλ θαη έγθαηξσλ απνθάζεσλ έρεη αλαρζεί ζε «εθ ησλ νπθ άλεπ» παξάγνληα επηηπρίαο γηα ηηο πεξηζζφηεξεο ζχγρξνλεο επηρεηξήζεηο θαη νξγαληζκνχο. Σαπηφρξνλα, ηα ηειεπηαία ρξφληα, κε ηελ αλάπηπμε λέσλ ηερλνινγηψλ θαη εθαξκνγψλ φπσο ε εμάπισζε ησλ θνηλσληθψλ δηθηχσλ, ε εθηεηακέλε ρξήζε smart phones, ε εγθαηάζηαζε αηζζεηήξσλ θ.α. ν φγθνο θαη ε κνξθή ησλ δεδνκέλσλ έρεη αιιάμεη δξακαηηθά, ελψ νη δπλαηφηεηεο αλάιπζεο θαη επεμεξγαζίαο απηψλ είλαη εληππσζηαθέο. Απηφ επέθεξε κεγάιεο αιιαγέο ζην πσο αληηιακβάλνληαη ηε ιήςε απνθάζεσλ νη εηαηξίεο θαη νξγαληζκνί. Πιένλ, ηηο πεξηζζφηεξεο θνξέο, αληί λα αλαπηχζζνπλ κνληέια, μεθηλνχλ απφ ηελ αλάιπζε ησλ δεδνκέλσλ πνπ θαηέρνπλ, ηα ζπλδπάδνπλ κε αιιά πνπ ζπιιέγνπλ απφ δηάθνξεο πεγέο θαη ηα κνληέια πξνθχπηνπλ απηφκαηα κε ηε κνξθή πξνηχπσλ. Όπσο δηαηππψλεη πεξηεθηηθά ην Wired Magazine [Αχγνπζηνο 2008]: Η αλαδήηεζε ηεο γλψζεο άξρηδε κε κεγάιεο ζεσξίεο. Πιένλ μεθηλά κε ηεξάζηηνπο φγθνπο δεδνκέλσλ. Oη φξνη Big Data, Business Analytics θαη Data Science βξίζθνληαη πιένλ ζην επίθεληξν ησλ δξαζηεξηνηήησλ ησλ ΙΣ ηκεκάησλ εηαηξηψλ θαη νξγαληζκψλ, αλεμαξηήησο κεγέζνπο. 1.1 Big Data & Business Analytics Έλαο απιφο νξηζκφο πνπ ζα κπνξνχζε λα ρξεζηκνπνηεζεί γηα ηελ πεξηγξαθή ησλ Μεγάισλ Γεδνκέλσλ (Big Data) είλαη ν εμήο: «Managing, analyzing and mining data, no matter how large it is, what format it has and how fast is produced.» Όπσο κπνξεί λα παξαηεξήζεη θαλείο εχθνια, δελ αθνξά ην κέγεζνο, ηνλ ηχπν ή ην είδνο ησλ δεδνκέλσλ, αιιά έλα ζχλνιν δηαδηθαζηψλ πνπ απαηηνχλ γλψζεηο πιεξνθνξηθήο, ζηαηηζηηθήο, κεραληθήο εθκάζεζεο θαη δηνίθεζεο επηρεηξήζεσλ, πνπ απιά εκπιέθνπλ θαη ρξεζηκνπνηνχλ δεδνκέλα. Πνιιέο θνξέο αλαθέξεηαη ζηε ζρεηηθή βηβιηνγξαθία φηη ηα Μεγάια Γεδνκέλα ραξαθηεξίδνληαη απφ ηα 3Vs: Volume, Variety, Velocity. Ο κεγάινο φγθνο (Volume) δεδνκέλσλ ραξαθηεξίδεη ηηο κνληέξλεο εθαξκνγέο, ελψ ηα δεδνκέλα έξρνληαη ζε δηάθνξεο κνξθέο, φπσο ζρεζηαθά δεδνκέλα, εηθφλα, ήρνο, video (Variety.) Σέινο, ε παξαγσγή ησλ δεδνκέλσλ κπνξεί λα γίλεηαη ζε πνιχ κεγάινπο ξπζκνχο (π.ρ. tweets, financial streams, αηζζεηήξεο) θαη ε εμαγσγή ζπκπεξαζκάησλ λα πξέπεη λα γίλεη ζε πξαγκαηηθφ ρξφλν (Velocity.) Η Δηθφλα 1 απφ ηε κειέηε ηνπ The Data Warehousing Institute (TDWI) «The TDWI Best Practices Report, Big Data Analytics, 4th Quarter 2011» δίλεη κία θαιή πεξηγξαθή ησλ 3Vs. Δικόνα 1: The 3Vs, Volume, Variety, Velocity (πηγή: TDWI, 4th Quarter 2011)

4 H αλάγθε δηαρείξηζεο ησλ δεδνκέλσλ ζε εθαξκνγέο Big Data νδήγεζε ζηελ αλάπηπμε κίαο λέαο γεληάο ζπζηεκάησλ, κνληέισλ θαη πξνγξακκαηηζηηθψλ εξγαιείσλ πνπ αθφκα βξίζθηαη ζε εκβξπαθφ ζηάδην- φπσο: Map Reduce, Hadoop θαη νηθνζχζηεκα απηνχ, NoSQL, θ.α., ηερλνινγίεο πνπ επηηξέπνπλ ηελ παξάιιειε επεμεξγαζία δεδνκέλσλ ζε κεγάιε θιίκαθα θαη κε fault-tolerant ηξφπν. Σαπηφρξνλα, ε αμηνπνίεζε απηψλ ησλ δεδνκέλσλ γηα ηελ παξαγσγή analytics απαηηνχλ ηθαλφηεηεο θαη γλψζεηο ζε έλα επξχ πεδίν αληηθεηκέλσλ, φπσο ζηαηηζηηθή, ηερλεηή λνεκνζχλε θαη ηερληθέο κεραληθήο εθκάζεζεο, επηρεηξεζηαθή έξεπλα, δηνίθεζε επηρεηξήζεσλ, θ.α. 1.2 Data Science Σα ηειεπηαία δχν ρξφληα έρεη αλαδεηρζεί έλαο λένο ξφινο ζηηο εηαηξίεο θαη ηνπο νξγαληζκνχο, κε ηελ νλνκαζία data scientist. Όπσο αλαθέξεη ην πεξηνδηθφ Economist («A special report on managing information: Data, data everywhere», Φεβξνπάξηνο 2010): «Οη Γηεπζπληέο Σερλνινγίαο έρνπλ εμειηρζεί ζε κία αλψηεξε ζέζε ζηελ εθηειεζηηθή ηεξαξρία κία εηαηξίαο θαη κία λέα ηδηφηεηα ζηειέρνπο έρεη αλαδεηρζεί, ν επηζηεκνληθφο ππεχζπλνο δεδνκέλσλ (the data scientist), ν νπνίνο ζπλδπάδεη ηα πξνζφληα ελφο ηθαλνχ πξνγξακκαηηζηή, ζηαηηζηηθνχ θαη αθεγεηή κε ζθνπφ λα αλαθαιχςεη ηνπο ζβψινπο ρξπζνχ πνπ ππάξρνπλ θάησ απφ ηεξάζηηνπο φγθνπο δεδνκέλσλ. Ο Hal Varian, Chief Economist ηεο Google, πξνβιέπεη φηη ην επάγγεικα ηνπ data scientist ζα γίλεη ζχληνκα έλα απφ ηα πιένλ ειθπζηηθά. Όπσο εμεγεί, ηα δεδνκέλα πιένλ είλαη επξέσο δηαζέζηκα, απηφ πνπ είλαη ζπάλην είλαη ε ηθαλφηεηα εμαγσγήο γλψζεο απφ απηά.» Δπίζεο, ν Tom Davenport ζε πξφζθαην άξζξν ηνπ «Data Scientist: The Sexiest Job of the 21st Century» ζην Harvard Business Review (Οθηψβξηνο 2012), επηρεηξεκαηνινγεί γηα ηελ αλαγθαηφηεηα ηέηνησλ επαγγεικαηηψλ, ελψ επηζεκαίλεη ηε κεγάιε έιιεηςε αλζξψπσλ ζε απηφλ ηνλ θιάδν. Η ζπκβνπιεπηηθή εηαηξία McKinsey ζηε κειέηε ηεο «Big Data: The next frontier for innovation, competition and productivity» (Ινχληνο 2011), πξνβιέπεη φηη ζα ρξεηαζηνχλ data scientists θαη φηη πεξίπνπ 1,5 εθαη. managers ζα πξέπεη λα εθπαηδεπηνχλ ζηε ρξήζε θαη αλάιπζε δεδνκέλσλ. Μία πην πξφζθαηε κειέηε ηεο Gartner (Οθηψβξηνο 2012), είλαη αθφκα πην αηζηφδνμε: 1,9 εθαη. ζέζεηο εξγαζίαο γηα data scientists θαη άιια 4 εθαη. γηα ηελ ππνζηήξημε απηψλ κφλν ζηηο Η.Π.Α. Yπάξρεη κεγάιε ζπδήηεζε γηα ην πνηεο είλαη νη ηθαλφηεηεο θαη γλψζεηο πνπ ζπλζέηνπλ ην ππφβαζξν ηνπ data scientist. Καηαξράο ζα πξέπεη λα έρεη πνιχ θαιή, βαζεηά γλψζε ησλ ζεκάησλ πνπ αθνξνχλ ηε δηαρείξηζε δεδνκέλσλ: κνληεινπνίεζε δεδνκέλσλ, γιψζζεο εξσηεκάησλ/αλάθηεζεο δεδνκέλσλ, επεμξγαζία, απνζήθεπζε, ελλνπνίεζε. Θα πξέπεη επίζεο λα έρεη πνιχ θαιφ καζεκαηηθφ ππφβαζξν ψζηε λα κπνξεί λα θαηαλνήζεη/ρεηξηζηεί ηηο ζηαηηζηηθέο κεζφδνπο θαη ηηο ηερληθέο machine learning πνπ απαηηνχληαη ζηελ εμαγσγή πξνηχπσλ. Σέινο, ζα πξέπεη λα είλαη ζρεηηθά αλήζπρν πλεχκα θαη λα γλσξίδεη θάπνηεο αξρέο ζχγρξνλνπ management, ψζηε λα αθνινπζεί ηηο εμειίμεηο ηνπ ζεκεξηλνχ ηερλνινγηθνχ θφζκνπ θαη λα έρεη κία αληίιεςε πσο κπνξνχλ λα ρξεζηκνπνηεζνχλ ηα δεδνκέλα ζε έλα εηαηξηθφ πεξηβάιινλ. Η Δηθφλα 2 ζπλνςίδεη ηνπο βαζηθνχο ηνκείο πνπ ζπλεηζθέξνπλ ζηηο γλψζεηο ελφο data scientist. Δικόνα 2: Βαζικοί ηομείρ γνώζεων Data Science

5 Απηή ηε ζηηγκή, ηα παλεπηζηήκηα δηεζλψο πνπ πξνζθέξνπλ εμεηδηθεπκέλα πξνγξάκκαηα ζηνλ ηνκέα ησλ Big Data & Analytics είλαη ειάρηζηα. Σα πξψηα εμεηδηθεπκέλα πξφγξακκαηα δεκηνπξγήζεθαλ ζηα παλεπηζηήκηα Northwestern θαη Carnegie Mellon ην επηέκβξην ηνπ Πνιχ ζνβαξέο πξνζπάζεηεο είλαη ε δεκηνπξγία ηνπ Institute of Data Sciences and Engineering ζην Columbia University ( θαη ηνπ Data Science Center ζην New York University ( Καη ζηηο δχν πεξηπηψζεηο, ζε απηά ηα Ιλζηηηνχηα ζπκκεηέρνπλ ηκήκαηα φινπ ηνπ παλεπηζηεκίνπ. ην ηκήκα Γηνηθεηηθήο Δπηζηήκεο θαη Σερλνινγίαο ηνπ Οηθνλνκηθνχ Παλεπηζηεκίνπ Αζελψλ, αλαγλσξίδνληαο λσξίο ηελ αμία ησλ Big Data θαη ησλ Business Analytics, αιιά θαη ηε ζπλάθεηα κε ην αληηθείκελν ηνπ ηκήκαηφο καο, εθπνλήζακε πξηλ δχν ρξφληα κία κειέηε ζθνπηκφηεηαο ελφο πξνγξάκκαηνο εμεηδίθεπζεο/κεηαπηπρηαθνχ ζε απηφ ην ζέκα, δηεξεπλψληαο θαη ζπιιέγνληαο απφςεηο απφ θαζεγεηέο ζε κεγάια παλεπηζηήκηα ηεο Ακεξηθήο. Σν παξφλ πξφγξακκα είλαη ην απνηέιεζκα απηήο ηεο πξνζπάζεηαο.

6 2 ΚΟΠΟ ΣΟΤ ΠΡΟΓΡΑΜΜΑΣΟ Σν πξφγξακκα πξνζθέξεηαη γηα δεχηεξε θνξά ην αθαδεκατθφ έηνο Σν πξφγξακκα θαηά ην αθαδεκατθφ έηνο γλψξηζε ηδηαίηεξε επηηπρία θαη ε δηαρείξηζή ηνπ βνήζεζε ζην λα αλαγλσξηζηνχλ ηα ζεκαληηθά θαη ιηγφηεξν ζεκαληηθά ραξαθηεξηζηηθά ελφο ηέηνηνπ πξνγξάκκαηνο, κε απνηέιεζκα λα ππάξρνπλ κηθξέο αιιαγέο ζηε δνκή ηνπ. ην λέν θχθιν έρεη δνζεί κεγαιχηεξε έκθαζε ζε εξγαιεία, ζπζηήκαηα, πινπνηήζεηο θαη case studies. Δπίζεο, έρεη δνζεί κεγαιχηεξν βάξνο ζε ζπγθεθξηκέλα καζήκαηα (π.ρ. ηαηηζηηθή, Business Process Management, θ.α.) Πιένλ, ην πξφγξακκα ζπνπδψλ απνηειείηαη θαηά 50% απφ καζήκαηα, 25% απφ εξγαιεία θαη ζπζηήκαηα θαη 25% απφ εθαξκνγέο θαη case studies. Ο ζθνπφο ηνπ πξνγξάκκαηνο είλαη λα πξνεηνηκάζεη ζηειέρε ηεο επηζηήκεο ησλ δεδνκέλσλ (data scientists), ηα νπνία λα κπνξνχλ λα ζπλδπάδνπλ κε ηνλ θαιχηεξν ηξφπν δηνηθεηηθέο γλψζεηο, ηερληθέο δηαρείξηζεο δεδνκέλσλ θαη αλαιπηηθά εξγαιεία, βαζηζκέλα θπξίσο ζηε ζηαηηζηηθή θαη ζηε κεραληθή εθκάζεζε, κε ζθνπφ ηε βέιηηζηε ιήςε επηρεηξεκαηηθψλ απνθάζεσλ. Γειαδή λα κπνξνχλ λα ρεηξηζηνχλ ηηο πιεξνθνξίεο ελφο γξήγνξα θαη ζπλερψο κεηαβαιιφκελνπ θφζκνπ, πινχζηνπ ζε δεδνκέλα, κε έλαλ ηξφπν επέιηθην, απνδνηηθφ θαη απνηειεζκαηηθφ γηα ην πεξηβάιινλ ζην νπνίν δξαζηεξηνπνηνχληαη. Γηα λα επηηεπρζεί απηφ ζα πξέπεη λα απνθηήζνπλ γλψζεηο θαη εκπεηξία ζε ηέζζεξηο βαζηθνχο ηνκείο: (α) δηαρείξηζε δεδνκέλσλ, (β) ζηαηηζηηθή θαη καζεκαηηθά ιήςεο απνθάζεσλ, (γ) δηνηθεηηθή επηζηήκε θαη (δ) εξγαιεία θαη ζπζηήκαηα παξαγσγήο analytics.

7 3 ΠΡΟΦΙΛ ΤΠΟΦΗΦΙΧΝ & ΓΙΑΓΙΚΑΣΙΚΑ 3.1 Προθίλ Τποψηθίων Σν πξνηεηλφκελν ππφβαζξν ησλ ππνςεθίσλ είλαη Σεσνολογικό (πιεξνθνξηθή, κεραληθνί), Θεηικέρ Δπιζηήμερ (καζεκαηηθά, θπζηθή), Γιοικηηική Δπιζηήμη, αιιά νπνηνδήπνηε ππφβαζξν ζα ιεθζεί ππφςε, αλάινγα θαη κε ηηο επηδφζεηο ηνπ ππνςεθίνπ. Η πξνυπεξεζία δελ είλαη απαξαίηεηε, αιιά επηζπκεηή. Σν αθφινπζν ζρφιην γηα ην πξνθίι ησλ ππνςεθηψλ αλήθεη ζηνλ θαζεγεηή ηνπ Stern Business School ηνπ παλεπηζηεκίνπ ηεο Νέαο Τφξθεο (NYU) θαη εηδηθνχ ηεο Google γηα anti-fraud ζην Google Adwords θ. Alex Tuzhilin, ν νπνίνο βνήζεζε ζηε δηακφξθσζε ηνπ παξφληνο πξνγξάκκαηνο: «Η ειάρηζηε απαίηεζε: νη θνηηεηέο λα γλσξίδνπλ θάηη απφ ηερλνινγία θαη business (κε ηνλ φξν ηερλνινγία, ελλνψ λα έρνπλ ππφβαζξν πιεξνθνξηθήο ή κεραληθνχ.) Δπίηεδεο έρσ αθήζεη ην «θάηη» λα είλαη αζαθέο. Η ρεηξφηεξε πεξίπησζε είλαη θάπνηνο πνπ γλσξίδεη αξθεηά απφ business θαη ιίγα απφ ηερλνινγία. Σφηε ζα απαηηεζνχλ δηνξζσηηθέο ελέξγεηεο, πνπ γεληθά είλαη δχζθνιν. Δμ νξηζκνχ, ν θνηηεηηθφο πιεζπζκφο ζαο ζα είλαη bi-modal θαη ε πξφθιεζε γηα ην πξφγξακκά ζαο ζα είλαη λα θαηαθέξεη λα ζπλππάξμνπλ. Σν πιενλέθηεκα είλαη φηη κπνξείηε λα ηνπο θάλεηε λα κάζνπλ ν έλαο απφ ηνλ άιινλ θαηά ηε δηάξθεηα ησλ projects θαη κπνξείηε λα ρξεζηκνπνηήζεηε απηφ ην diversity card ζε δηάθνξα άιια ζεκεία. Ίζσο ζα «έζπξσρλα» απηφ ην business/technology tradeoff πξνο ηελ πιεπξά ηεο ηερλνινγίαο, θαζψο θάπνηα καζήκαηα κπνξεί λα είλαη αξθεηά ηερληθά θαη θάπνηνη θνηηεηέο θαηεχζπλζεο business κε πεξηνξηζκέλε γλψζε ηερλνινγίαο κπνξεί λα αληηκεησπίζνπλ δπζθνιίεο.» Σν πξφγξακκα ζα δερηεί κέρξη 35 ζςμμεηέσονηερ γηα ην αθαδεκατθφ έηνο ηοιχεία ηης Αίηηζης H αίηεζε κπνξεί λα παξαιεθζεί ειεθηξνληθά ζηνλ ηζηφηνπν ή απφ ηνλ ηζηφηνπν ηνπ ΚΔΚ θαη λα απνζηαιεί κε . Η αίηεζε πεξηιακβάλεη πξνζσπηθά ζηνηρεία, ηίηινπο θαη πιεξνθνξίεο ζπνπδψλ, βαζκνινγίεο πηπρίσλ, απφδεημε γλψζεο ηεο αγγιηθήο γιψζζαο, πεξηγξαθή πξνυπεξεζίαο (αλ ππάξρεη) θαη ηα ζηνηρεία δχν αηφκσλ πνπ ζα απνζηείινπλ ζπζηαηηθέο επηζηνιέο γηα ηνλ αηηνχληα/ηελ αηηνχζα αλ απαηηεζεί. 3.3 Υορήγηζη Πιζηοποιηηικών ηνπο επηηπγρφληεο ζε φια ηα εμεηαδφκελα καζήκαηα, ην KEK ηνπ Οηθνλνκηθνχ Παλεπηζηεκίνπ Αζελψλ ρνξεγεί Πηζηνπνηεηηθφ Δπαγγεικαηηθήο Δθπαίδεπζεο θαη Καηάξηηζεο, κε Απφθαζε ηεο πγθιήηνπ ηνπ ΟΠΑ (ζηηο 13/01/2000), ζηελ νπνία αλαγξάθεηαη ν κέζνο φξνο ηεο βαζκνινγίαο φισλ ησλ εμεηαδφκελσλ καζεκάησλ θαη ε αληίζηνηρε επίδνζε. Οη Βαζκνί απφ 60 έσο 75 απνηεινχλ ηελ θαηεγνξία «Καιψο», απφ 76 έσο 90 «Λίαλ Καιψο» θαη απφ «Άξηζηα».

8 3.4 Γιάρκεια Προγράμμαηος Υώρος Τλοποίηζης Η δηάξθεηα ηνπ πξνγξάκκαηνο είλαη 250 ώπερ. Σα καζήκαηα ζα γίλνληαη ζηηο αίζνπζεο ηνπ KEK ηνπ Οηθνλνκηθνχ Παλεπηζηεκίνπ Αζελψλ (νδφο Κεθαιιελίαο 46 θαη Παηεζίσλ, 6νο φξνθνο). Ηκέξεο θαη ψξεο καζεκάησλ: Γεπηέξα θαη Σεηάξηε, 18:00 21:00. Σα καζήκαηα ζα αξρίζνπλ ηνλ Οθηψβξην ηνπ 2014 θαη ζα νινθιεξσζνχλ ηνλ Ινχλην ηνπ Oη αθξηβείο εκεξνκελίεο ζα αλαθνηλσζνχλ ζην Γίδακηρα Σα δίδαθηξα ηνπ πξνγξάκκαηνο είλαη δχν ρηιηάδεο επηαθφζηα επξψ (2.700,00 ) θαη ζα πξέπεη λα θαηαβιεζνχλ ζε 3 δφζεηο, θαηά ηε δηάξθεηα ηνπ αθαδεκατθνχ έηνπο Τπάξρεη δπλαηφηεηα επηζηξνθήο ηνπ ζπλνιηθνχ θφζηνπο ηνπ πξνγξάκκαηνο ή κέξνπο απηνχ ζηηο επηρεηξήζεηο, κέζσ ηεο εξγνδνηηθήο εηζθνξάο 0,45% ηνπ ΟΑΔΓ (πξφγξακκα ΛΑΔΚ). 3.6 Δπικοινωνία Κ.Δ.Κ. Οηθνλνκηθνχ Παλεπηζηήκηνπ Αζελψλ, Κεθαιιελίαο 46 & Παηεζίσλ, Αζήλα, Πέηξνο Καιθάλεο (petros@rc.aueb.gr), Νεθηαξία Λπθνχξε, Ρνδίηε Ράληα Σει. :

9 ΕΡΓΑΛΕΙΑ/ΣΥΣΤΗΜΑΤΑ CASE STUDIES ΒΑΣΙΚΑ ΜΑΘΗΜΑΤΑ 4 ΠΡΟΓΡΑΜΜΑ ΠΟΤΓΧΝ & ΠΔΡΙΓΡΑΦΔ ΜΑΘΗΜΑΣΧΝ Σν πξφγξακκα ζπνπδψλ εκθαλίδεηαη ζπλνπηηθά ζηνλ Πίλαθα 1. Τίτλος Ώρες Εισηγητής Modern Data Management 12 Αναπ. Καθ. Βαζίλης Βαζζάλος Statistics for Analytics I 15 Δπικ. Καθ. Παναγιώηης Τζιαμσρηζής Business Intelligence 12 Αναπ. Καθ. Γαμιανός Χαηζηανηωνίοσ Large Scale Optimization 12 Καθ. Χρήζηος Ταρανηίλης Statistics for Analytics II 18 Αναπ. Καθ. Γημήηρης Καρλής Business Process Management 15 Αναπ. Καθ. Αγγελική Ποσλσμενάκοσ Mining Big Datasets 12 Αναπ. Καθ. Γιάννης Κωηίδης Big Data Systems 12 Αναπ. Καθ. Γαμιανός Χαηζηανηωνίοσ Engineering Big Data Systems 9 Καθ. Γιομήδης Σπινέλλης Innovation & Entrepreneurship 9 Αναπ. Καθ. Καηερίνα Πραμαηάρη Text Analytics and Social Media Analytics 12 Γρ. Χάρης Παπαγεωργίοσ Social Network Analysis 15 Καθ. Γιώργος Γιαγλής Privacy and Personal Data Protection 9 Αναπ. Καθ. Λίλιαν Μήηροσ Data Visualization 3 <Θα ανακοινωθεί> Healthcare Analytics 3 <Θα ανακοινωθεί> Public Sector Analytics 3 <Θα ανακοινωθεί> Energy Management Analytics 3 <Θα ανακοινωθεί> Learning Analytics 3 <Θα ανακοινωθεί> Transportation Analytics 3 <Θα ανακοινωθεί> Telecom Analytics 3 <Θα ανακοινωθεί> Marketing Analytics 3 <Θα ανακοινωθεί> Human Resource Analytics 3 <Θα ανακοινωθεί> Behavioural Analytics 3 <Θα ανακοινωθεί> Hadoop/Amazon EC2/MapReduce/ Pig/Hive 15 <Θα ανακοινωθεί> R 15 Miguel Forte, Workable & U of Edinburgh MongoDB 9 Miguel Forte, Workable & U of Edinburgh Redis/Neo4j 6 <Θα ανακοινωθεί> IBM Tools 12 IBM SAS Tools 12 SAS Thesis Capstone Project / Thesis Πίνακαρ 1: ςνοπηικό Ππόγπαμμα ποςδών

10 Αθνινπζεί αλαιπηηθή πεξηγξαθή ησλ βαζηθψλ καζεκάησλ. 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). Large Scale Optimization 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 data management and analysis 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 ΔΙΗΓΗΣΔ & ΒΙΟΓΡΑΦΙΚΑ Οη δηδάζθνληεο ζην πξφγξακκα εμεηδίθεπζεο «Big Data & Business Analytics» είλαη θπξίσο θαζεγεηέο ηνπ Οηθνλνκηθνχ Παλεπηζηεκίνπ Αζελψλ, κε κεγάιε εκπεηξία ζην ρψξν ηνπο, πινχζηα εξεπλεηηθή δξαζηεξηφηεηα θαη θάηνρνη δηδαθηνξηθνχ δηπιψκαηνο απφ θνξπθαία παλεπηζηήκηα δηεζλψο, φπσο: Columbia University, Imperial University, London School of Economics, Stanford University, University of Frankfurt, University of Maryland, University of Minnesota. Αναπ. Καθ. Βαζίληρ Βαζζάλορ, Σμήμα Πληποθοπικήρ, 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. Γιώπγορ Γιαγλήρ, Σμήμα Γιοικηηικήρ Δπιζηήμηρ & Σεσνολογίαρ, 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. Γιώπγορ Ιωάννος, Σμήμα Γιοικηηικήρ Δπιζηήμηρ & Σεσνολογίαρ, 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. Γημήηπηρ Καπλήρ, Σμήμα ηαηιζηικήρ, 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. Γιάννηρ Κωηίδηρ, Σμήμα Πληποθοπικήρ, 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. Αναπ. Καθ. Λίλιαν Μήηπος, Σμήμα Μησαν. Πληπ. & Δπικ. ςζηημάηων, Πανεπ. Αιγαίος 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. Γπ. Υάπηρ Παπαγεωπγίος, Ινζηιηούηο Δπεξεπγαζίαρ Λόγος & Δπεςνηηικό Κένηπο 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 Ποςλςμενάκος, Σμήμα Γιοικηηικήρ Δπιζηήμηρ & Σεσνολογίαρ, 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 Αναπ. Καθ. Καηεπίνα Ππαμαηάπη, Σμήμα Γιοικηηικήρ Δπιζηήμηρ & Σεσνολογίαρ, 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. Γιομήδηρ πινέλληρ, Σμήμα Γιοικηηικήρ Δπιζηήμηρ & Σεσνολογίαρ, 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. Υπήζηορ Σαπανηίληρ, Σμήμα Γιοικηηικήρ Δπιζηήμηρ & Σεσνολογίαρ, 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. Δπικ. Καθ. Παναγιώηηρ Σζιαμςπηζήρ, Σμήμα ηαηιζηικήρ, 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. Αναπ. Καθ. Γαμιανόρ Υαηζηανηωνίος, Σμήμα Γιοικηηικήρ Δπιζηήμηρ & Σεσνολογίαρ, Ο.Π.Α. Δπιζηημονικόρ Τπεύθςνορ ηος Ππογπάμμαηορ 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.