Publications de Parisa Haghani

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

In this paper we consider distributed K-Nearest Neighbor (KNN) search and range query processing in high dimensional data. Our approach is based on Locality Sensitive Hashing (LSH) which has proven very efficient in answering KNN queries in centralized set ...

Web 2.0 streams, like blog postings, microblogging tweets, or RSS feeds from online communities, offer a wealth of latest news about real-world events and societal discussion. From a user’s perspective, it becomes harder and harder to get a decent overview ...

Ranking queries, which return only a subset of results matching a user query, have been studied extensively in the past decade due to their importance in a wide range of applications. In this thesis, we study ranking queries in novel environments and setti ...

GridVine is a Peer Data Management System based on a decentralized access structure. Built following the principle of data independence, it separates a logical layer -- where data, schemas and mappings are managed -- from a physical layer consisting of a s ...

We tackle the problem of disambiguating entities on the Web. We propose a user-driven scheme where graphs of entities -- represented by globally identifiable declarative artifacts -- self-organize in a dynamic and probabilistic manner. Our solution has the ...

We consider K-Nearest Neighbor search for high dimensional data in large-scale structured Peer-to-Peer networks. We present an efficient mapping scheme based on p-stable Locality Sensitive Hashing to assign hash buckets to peers in a Chord-style overlay ne ...

We study the problem of continuous monitoring of top-k queries over multiple non-synchronized streams. Assuming a sliding window model, this general problem has been a well addressed research topic in recent years. However, most approaches assume the strea ...

The rise of the Web 2.0 has made content publishing easier than ever. Yesterday’s passive consumers are now active users who generate and contribute new data to the web at an immense rate. We consider evaluating data driven aggregation queries which arise ...

Web 2.0 portals have made content generation easier than ever with millions of users contributing news stories in form of posts in weblogs or short textual snippets as in Twitter. Efficient and effective filtering solutions are key to allow users stay tune ...

Wireless Sensor Networks (WSNs) rely on in-network aggregation for efficiency, however, this comes at a price: A single adversary can severely influence the outcome by contributing an arbitrary partial aggregate value. Secure in-network aggregation can det ...

Parisa Haghani

Chattez avec Graph Search

Distributed Similarity Search in High Dimensions Using Locality Sensitive Hashing

Efficient monitoring of personalized hot news over Web 2.0 streams

Efficient Processing of Ranking Queries in Novel Applications

Self-Organizing Schema Mappings in the GridVine Peer Data Management System

idMesh: graph-based disambiguation of linked data

LSH At Large - Distributed KNN Search in High Dimensions

Evaluating Top-k Queries over Incomplete Data Streams

Tracking Hot-k Items over Web 2.0 Streams

The gist of everything new: personalized top-k processing over web 2.0 streams

Efficient and Robust Secure Aggregation for Sensor Networks