|Name||Dr. Ghislain Fourny|
ETH Zürich, STF H 311
|Telephone||+41 44 632 31 55|
|252-0341-01L||Information Retrieval||4 credits||2V + 1U||G. Fourny|
|Abstract||This course gives an introduction to information retrieval with a focus on text documents and unstructured data.|
Main topics comprise document modelling, various retrieval techniques, indexing techniques, query frameworks, optimization, evaluation and feedback.
|Objective||We keep accumulating data at an unprecedented pace, much faster than we can process it. While Big Data techniques contribute solutions accounting for structured or semi-structured shapes such as tables, trees, graphs and cubes, the study of unstructured data is a field of its own: Information Retrieval.|
After this course, you will have in-depth understanding of broadly established techniques in order to model, index and query unstructured data (aka, text), including the vector space model, boolean queries, terms, posting lists, dealing with errors and imprecision.
You will know how to make queries faster and how to make queries work on very large datasets. You will be capable of evaluating the quality of an information retrieval engine.
Finally, you will also have knowledge about alternate models (structured data, probabilistic retrieval, language models) as well as basic search algorithms on the web such as Google's PageRank.
2. Boolean retrieval: the basics of how to index and query unstructured data.
3. Term vocabulary: pre-processing the data prior to indexing: building the term vocabulary, posting lists.
4. Tolerant retrieval: dealing with spelling errors: tolerant retrieval.
5. Index construction: scaling up to large datasets.
6. Index compression: how to improve performance by compressing the index in various ways.
7. Ranked retrieval: how to ranking results with scores and the vector space model
8. Scoring in a bigger picture: taking ranked retrieval to the next level with various improvements, including inexact retrieval
9. Probabilistic information retrieval: how to leverage Bayesian techniques to build an alternate, probabilistic model for information retrieval
10. Language models: another alternate model based on languages, automata and document generation
11. Evaluation: precision, recall and various other measurements of quality
12. Web search: PageRank
The lecture structure will follow the pedagogical approach of the book (see material).
The field of information retrieval also encompasses machine learning aspects. However, we will make a conscious effort to limit overlaps, and be complementary with, the Introduction to Machine Learning lecture.
|Literature||C. D. Manning, P. Raghavan, H. Schütze, Introduction to Information Retrieval, Cambridge University Press.|
|Prerequisites / Notice||Prior knowledge in elementary set theory, logics, linear algebra, data structures, abstract data types, algorithms, and probability theory (at the Bachelor's level) is required, as well as programming skills (we will use Python).|
|252-3900-00L||Big Data for Engineers |
This course is not intended for Computer Science and Data Science MSc students!
|6 credits||2V + 2U + 1A||G. Fourny|
|Abstract||This course is part of the series of database lectures offered to all ETH departments, together with Information Systems for Engineers. It introduces the most recent advances in the database field: how do we scale storage and querying to Petabytes of data, with trillions of records? How do we deal with heterogeneous data sets? How do we deal with alternate data shapes like trees and graphs?|
|Objective||This lesson is complementary with Information Systems for Engineers as they cover different time periods of database history and practices -- you can even take both lectures at the same time.|
The key challenge of the information society is to turn data into information, information into knowledge, knowledge into value. This has become increasingly complex. Data comes in larger volumes, diverse shapes, from different sources. Data is more heterogeneous and less structured than forty years ago. Nevertheless, it still needs to be processed fast, with support for complex operations.
This combination of requirements, together with the technologies that have emerged in order to address them, is typically referred to as "Big Data." This revolution has led to a completely new way to do business, e.g., develop new products and business models, but also to do science -- which is sometimes referred to as data-driven science or the "fourth paradigm".
Unfortunately, the quantity of data produced and available -- now in the Zettabyte range (that's 21 zeros) per year -- keeps growing faster than our ability to process it. Hence, new architectures and approaches for processing it were and are still needed. Harnessing them must involve a deep understanding of data not only in the large, but also in the small.
The field of databases evolves at a fast pace. In order to be prepared, to the extent possible, to the (r)evolutions that will take place in the next few decades, the emphasis of the lecture will be on the paradigms and core design ideas, while today's technologies will serve as supporting illustrations thereof.
After visiting this lecture, you should have gained an overview and understanding of the Big Data landscape, which is the basis on which one can make informed decisions, i.e., pick and orchestrate the relevant technologies together for addressing each business use case efficiently and consistently.
|Content||This course gives an overview of database technologies and of the most important database design principles that lay the foundations of the Big Data universe. |
It targets specifically students with a scientific or Engineering, but not Computer Science, background.
We take the monolithic, one-machine relational stack from the 1970s, smash it down and rebuild it on top of large clusters: starting with distributed storage, and all the way up to syntax, models, validation, processing, indexing, and querying. A broad range of aspects is covered with a focus on how they fit all together in the big picture of the Big Data ecosystem.
No data is harmed during this course, however, please be psychologically prepared that our data may not always be in normal form.
- physical storage: distributed file systems (HDFS), object storage(S3), key-value stores
- logical storage: document stores (MongoDB), column stores (HBase)
- data formats and syntaxes (XML, JSON, RDF, CSV, YAML, protocol buffers, Avro)
- data shapes and models (tables, trees)
- type systems and schemas: atomic types, structured types (arrays, maps), set-based type systems (?, *, +)
- an overview of functional, declarative programming languages across data shapes (SQL, JSONiq)
- the most important query paradigms (selection, projection, joining, grouping, ordering, windowing)
- paradigms for parallel processing, two-stage (MapReduce) and DAG-based (Spark)
- resource management (YARN)
- what a data center is made of and why it matters (racks, nodes, ...)
- underlying architectures (internal machinery of HDFS, HBase, Spark)
- optimization techniques (functional and declarative paradigms, query plans, rewrites, indexing)
Large scale analytics and machine learning are outside of the scope of this course.
|Literature||Papers from scientific conferences and journals. References will be given as part of the course material during the semester.|
|Prerequisites / Notice||This course is not intended for Computer Science and Data Science students. Computer Science and Data Science students interested in Big Data MUST attend the Master's level Big Data lecture, offered in Fall.|
Requirements: programming knowledge (Java, C++, Python, PHP, ...) as well as basic knowledge on databases (SQL). If you have already built your own website with a backend SQL database, this is perfect.
Attendance is especially recommended to those who attended Information Systems for Engineers last Fall, which introduced the "good old databases of the 1970s" (SQL, tables and cubes). However, this is not a strict requirement, and it is also possible to take the lectures in reverse order.