EDBT 2026 Demo / reviewers in the wild / expert
Susanna Pirttikangas
dblp:57/2538
· DBLP profile ↗
5ranked-venue papers in the field
1as first author
1since 2021 · last 2022
0000-0003-2428-9948ORCID · verified
Domains — venue-derived; a paper can count in several
Big Data, Cloud & Distributed Data Systems · 2 (1 first)Database Systems & Data Management · 1Data Mining & Knowledge Discovery · 1Business Process & Enterprise Data · 1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | Situation Awareness for Autonomous Vehicles Using Blockchain-Based Service Cooperation
Huong Mai Nguyen, Tri Nguyen 0001, Teemu Leppänen, Juha Partala, Susanna Pirttikangas |
CAiSE | 5 |
| 2018 | SemanPhone: Combining Semantic and Phonetic Word Association in Verbal Learning ContextabstractThis paper proposes an effective way to discover and memorize new English vocabulary based on both semantic and phonetic associations. The method we proposed aims to automatically find out the most associated words of a given target word. The measurement of semantic association was achieved by calculating cosine similarity of two-word vectors, and the measurement of phonetic association was achieved by calculating the longest common subsequence of phonetic symbol strings of two words. Finally, the method was implemented as a web application. Jiyan Lu, Panos Kostakos 0001, Mourad Oussalah 0002, Susanna Pirttikangas |
ASONAM | 4 |
| 2016 | Experiences with smart city traffic pilotabstractThe infrastructure built in the City of Oulu provides rich information about the city environment and objects moving in it. We utilize this infrastructure in building an IoT system for data-intensive smart city services; by collecting data from real city environment and developing analysis methods for these data. We are building Smart City Traffic Pilot on top of the infrastructure to provide the functionality to collect the data and perform the analysis. Based on this experience, we present in this article requirements for data-intensive smart city services. Moreover, we describe four implemented use cases for utilizing rich data sources available in the smart city: situational picture, driving coach, real time reasoning, and mobile code. A lively collaboration between a large number of different actors is essential in realizing these use cases. Finally, we discuss how the use cases fulfill the requirements and the lessons we have learnt. Susanna Pirttikangas, Ekaterina Gilman, Xiang Su 0001, Teemu Leppänen, Anja Keskinarkaus, Mika Rautiainen, Mikko Pyykkönen, Jukka Riekki |
IEEE BigData | 1 |
| 2015 | Low latency analytics for streaming traffic data with Apache SparkabstractDemand for new efficient methods for processing large-scale heterogeneous data in real-time is growing. Currently, one key challenge in Big Data is performing low-latency analysis with real-time data. In vehicle traffic, continuous high speed data streams generate large data volumes. Harnessing new technologies is required to benefit from all the potential this data withholds. This work studies the state-of-the-art in distributed and parallel computing, storage, query and ingestion methods, and evaluates tools for periodical and real-time analysis of heterogeneous data. We also introduce a Big Data cloud platform with ingestion, analysis, storage and data query APIs to provide programmable environment for analytics system development and evaluation. Altti Ilari Maarala, Mika Rautiainen, Miikka Salmi, Susanna Pirttikangas, Jukka Riekki |
IEEE BigData | 4 |
| 2010 | Aquiba: An Energy-Efficient Mobile Sensing System for Collaborative Human Probes
Niwat Thepvilojanapong, Shin'ichi Konomi, Jun'ichi Yura, Takeshi Iwamoto, Susanna Pirttikangas, Yasuyuki Ishida, Masayuki Iwai, Yoshito Tobe, Hiroyuki Yokoyama, Jin Nakazawa, Hideyuki Tokuda |
DASFAA (2) | 5 |