Publications by Parisa Haghani

Efficient monitoring of personalized hot news over Web 2.0 streams

Karl Aberer, Parisa Haghani, Sebastian Michel

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 ...

2012

Tracking Hot-k Items over Web 2.0 Streams

Karl Aberer, Parisa Haghani, Sebastian Michel

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 ...

2011

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

Karl Aberer, Parisa Haghani, Sebastian Michel

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 ...

ACM2010

Efficient Processing of Ranking Queries in Novel Applications

Parisa Haghani

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 ...

EPFL2010

idMesh: graph-based disambiguation of linked data

Philippe Cudré-Mauroux, Karl Aberer, Michael Jost, Parisa Haghani

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 ...

ACM2009

Evaluating Top-k Queries over Incomplete Data Streams

Karl Aberer, Parisa Haghani, Sebastian Michel

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 ...

2009

Distributed Similarity Search in High Dimensions Using Locality Sensitive Hashing

Karl Aberer, Parisa Haghani, Sebastian Michel

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 ...

2009

LSH At Large - Distributed KNN Search in High Dimensions

Philippe Cudré-Mauroux, Karl Aberer, Parisa Haghani, Sebastian Michel

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 ...

2008

Efficient and Robust Secure Aggregation for Sensor Networks

Jean-Pierre Hubaux, Karl Aberer, Marcin Poturalski, Panagiotis Papadimitratos, Parisa Haghani

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 ...

2007

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

Philippe Cudré-Mauroux, Karl Aberer, Adriana Budura, Parisa Haghani

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 ...

2007