[2nd CFP] JBDR Special Issue on Big Data and Smart Cities

*** Apologies for cross posting. ***

********************* CALL FOR PAPERS *********************

SUBMISSION DUE DATE: 31st October 2014 (full paper)

SPECIAL ISSUE ON Big Data and Smart Cities (

Big Data Research

Guest Editor:

Freddy Lecue, IBM Research, Ireland
Achille Fokoue, IBM Research, US
Jeff Z. Pan, University of Aberdeen, UK
Huajun Chen, Zhejiang University, China


In a Smarter City, available resources are harnessed safely, sustainably
and efficiently to achieve positive, measurable economic and societal
outcomes. Data (and then information) from people, systems and things in
cities is the single most scalable resource available to City stakeholders
but difficult to publish, organize, discover, interpret, combine, analyze,
reason and consume, especially in such an heterogeneous environment. Indeed
data is big and exposed from heterogenous environments such as water,
energy, traffic or building.

Most of the challenges of Big Data in Smart Cities are multi-dimensional
and can be addressed from different multidisciplinary perspectives e.g.,
from Artificial Intelligence (Machine Learning, Semantic Web), Database,
Data Mining to Distributed Systems communities.

Enabling City information as a utility, through a robust (expressive,
dynamic, scalable) and (critically) a sustainable technology and socially
synergistic ecosystem, could drive significant benefits and opportunities.

While research efforts in Big Data have mostly focused on the later stages
of the process of making  sense of the sea of data (e.g. data analytics,
query answering, data visualization, etc), in the context of Smart Cities,
where heterogeneous data originates from multiple municipal and state
agencies with little to no coordination, major hurdles and issues continue
to impede progress toward these later stages. These key unaddressed issues
are often related to information exploration, access, and linking: e.g.,

1) How to efficiently figure out and access data sources relevant to a
given task?

2) How to discover implicit relevant links between these information
sources at the data level?

3) How to determine relevant data in the selected linked data sources?

Today, these challenges are tackled in mostly ad-hoc and labor intensive
data integration efforts.  It is becoming increasing clear that, without
the advent of novel, scalable and semi-automated data integration
techniques, this first data access and linking stage will soon represent a
major bottleneck to the whole process of extracting valuable information
from the increasing number data sources  and volume of data available to
decision makers.


Paper submission (1st stage): 31st October 2014
Reviewing process and revision submission (1st stage): 31st January 2015
Paper submission (2nd stage): 31st March 2015
Reviewing process and revision submission (2nd stage): 31st May 2015
Camera ready: 30th June 2015
Online Publication: 31st August 2015

Received on Monday, 29 September 2014 13:21:00 UTC