Java collections framework

Summary

The Java collections framework is a set of classes and interfaces that implement commonly reusable collection data structures. Although referred to as a framework, it works in a manner of a library. The collections framework provides both interfaces that define various collections and classes that implement them. Differences from Arrays Collections and arrays are similar in that they both hold references to objects and they can be managed as a group. However, unlike arrays, Collections do not need to be assigned a certain capacity when instantiated. Collections can grow and shrink in size automatically when objects are added or removed. Collections cannot hold primitive data types such as int, long, or double. Instead, Collections can hold wrapper classes such as , , or . Collections are generic and hence invariant, but arrays are covariant. This can be considered an advantage of generic objects such as when compared to arrays, because under circumstances, using the g

Official source

https://en.wikipedia.org/wiki/Java_collections_framework

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Les étudiants perfectionnent leurs connaissances en Java et les mettent en pratique en réalisant un projet de taille conséquente. Ils apprennent à utiliser et à mettre en œuvre les principaux types de collections (listes, ensembles, tables associatives), et examinent quelques patrons de conception.

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This course is intended for students who want to understand modern large-scale data analysis systems and database systems. It covers a wide range of topics and technologies, and will prepare students to be able to build such systems as well as read and understand recent research publications.

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Predicting behavior of Twitter Users

Adela Maznikar

Social networks today are great source of data which can be used and analyzed in different ways. In our project the main goal is to predict the behavior of the users, more accurately said: we try to predict what will a particular user tweet in the future, when given a training set of existing URL mentions. The topic has been previously explored in the paper Outtweeting the Twitterers-Predicting Information Cascades in Microblogs by Wojciech Galuba, Karl Aberer, Dipanjan Chakraborty, Zoran Despotovic, Wolfgang Kellerer and our work is the extension of their research. As mentioned in their paper, accurate predictions can be used for many purposes. Some of them are: personalized recommendation, ranking and filtering of the stream of tweets that the user sees etc. Also. it can be used in predicting in virality of a certain URL which can help in marketing campaigns for predicting the success of that campaign. One of the main tasks on this project is: Build a framework which can rank, evaluate different models of predictions with online data. We made a tool/framework in Java that can work with different models of predictions, the evaluator just need to specify the model of prediction with very little code and the framework offers a lot of support for calculating usually used parameters in the model so the prediction is made really easier once the model is known. However in order to test if our framework works correctly, we implemented two models which will be described further in the report and we were working with offline data.

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Auto-Collections for Scala

Dmytro Petrashko, Allan Vincent Roger Renucci

Modern programming languages such as Scala, Java and C# make extensive use of collections. A collection implementation represents a fixed choice in the dimensions of operation time and space utilization. Using the collection in a manner not consistent with this fixed choice can cause significant performance degradation. Often programmers have to choose between under-performing generic collection and specific data-structures at the cost of modularity and ex- tensibility. In his paper, we present and evaluate a generic collection library that automatically choose the appropriate collection implementations for an application. We implemented our solution in the Dotty compiler for Scala and showed that our implementation can choose optimal collections for com- mon basic algorithms.

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On A Generic Parallel Collection Framework

Martin Odersky, Aleksandar Prokopec, Tiark Rompf

As the number of cores increases in modern multiprocessors, it is becoming increasingly difficult to write general purpose applications that efficiently utilize this computing power. Most applications manipulate structured data. Modern languages and platforms provide collection frameworks with basic data structures like lists, hashtables and trees. These data structures have a range of predefined operations which include mapping, filtering or finding elements. Such bulk operations traverse the collection and process the elements sequentially. Their implementation relies on iterators, which are not applicable to parallel operations due to their sequential nature. We present an approach to parallelizing collection operations in a generic way, used to factor out common parallel operations in collection libraries. Our framework is easy to use and straightforward to extend to new collections. We show how to implement concrete parallel collections such as parallel arrays and parallel hash maps, proposing an efficient solution to parallel hash map construction. Finally, we give benchmarks showing the performance of parallel collection operations.

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