Publications by Milos Nikolic | EPFL Graph Search

Efficient Incremental Data Analysis

Many data-intensive applications require real-time analytics over streaming data. In a growing number of domains -- sensor network monitoring, social web applications, clickstream analysis, high-frequency algorithmic trading, and fraud detections to name a ...

EPFL2016

How to Win a Hot Dog Eating Contest: Distributed Incremental View Maintenance with Batch Updates

Christoph Koch, Milos Nikolic, Mohammad Dashti Rahmat Abadi

In the quest for valuable information, modern big data applications continuously monitor streams of data. These applications demand low latency stream processing even when faced with high volume and velocity of incoming changes and the user’s desire to ask ...

2016

DBToaster: higher-order delta processing for dynamic, frequently fresh views

Christoph Koch, Amir Shaikhha, Milos Nikolic, Daniel Lupei, Yanif Ahmad

Applications ranging from algorithmic trading to scientific data analysis require real-time analytics based on views over databases receiving thousands of updates each second. Such views have to be kept fresh at millisecond latencies. At the same time, the ...

Springer Verlag2014

LINVIEW: Incremental View Maintenance for Complex Analytical Queries

Christoph Koch, Milos Nikolic, Mohamed Elsayed Mohamed Ahmed El Seidy

Many analytics tasks and machine learning problems can be naturally expressed by iterative linear algebra programs. In this paper, we study the incremental view maintenance problem for such complex analytical queries. We develop a framework, called LINVIEW ...

2014

DBToaster: Higher-order Delta Processing for Dynamic, Frequently Fresh Views

Christoph Koch, Milos Nikolic, Yanif Ahmad

Applications ranging from algorithmic trading to scientific data analysis require realtime analytics based on views over databases that change at very high rates. Such views have to be kept fresh at low maintenance cost and latencies. At the same time, the ...

2012