Abstract
A rise in population, along with urbanization, has been causing an increase in demand for urban transportation services in the sub-Saharan Africa countries. In these countries, mobility of people is mainly ensured by bus services and a large-scale informal public transport service, which is known as paratransit (e.g., car rapides in Senegal, Tro Tros in Ghana, taxis in Uganda and Ethiopia, and Matatus in Kenya). Transport demand estimation is a challenging task, particularly in developing countries, mainly due to its expensive and time-consuming data collection requirements. Without accurate demand estimation, it is difficult for transport operators to provide their services and make other important decisions. In this paper, we present a methodology to estimate passenger demand for public transport services using cell phone data. Significant origins and destinations of inhabitants are extracted and used to build origin-destination matrices that resemble travel demand. Based on the inferred travel demand, we are able to reasonably suggest strategic locations for public transport services such as paratransit and taxi stands, as well as new transit routes. The outcome of this study can be useful for the development of policies that can potentially help fulfill the mobility needs of city inhabitants.

Abstract
User preferences are fairly dynamic, since users tend to exploit a wide range of information and modify their tastes accordingly over time. Existing models and formulations are too constrained to capture the complexity of this underlying phenomenon. In this paper, we investigate the interplay between user preferences and social networks over time. We propose to analyze user preferences dynamics with his/her social network modeled as a temporal network. First, we define a temporal preference model for reasoning with preferences. Then, we use evolving centralities from temporal networks to link with preferences dynamics. Our results indicate that modeling Twitter as a temporal network is more appropriated for analyzing user preferences dynamics than using just snapshots of static network.

Abstract
The blooming of different cloud data management infrastructures has turned multistore systems to a major topic in the nowadays cloud landscape. In this paper, we give an overview of the design of a Cloud Multidatastore Query Language (CloudMdsQL), and the implementation of its query engine. CloudMdsQL is a functional SQL-like language, capable of querying multiple heterogeneous data stores (relational, NoSQL, HDFS) within a single query that can contain embedded invocations to each data store's native query interface. The major innovation is that a CloudMdsQL query can exploit the full power of local data stores, by simply allowing some local data store native queries (e.g. a breadth-first search query against a graph database) to be called as functions, and at the same time be optimized.

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Abstract
Computer Assisted Qualitative Data Analysis Software (CAQDAS) may be defined as tools that help researchers developing qualitative research projects. These software packages help the users with tasks such as transcription analysis, writing and annotation, coding and text interpretation, recursive abstraction, content search and analysis, discourse analysis, data mapping, grounded theory methodology, among several other types of analysis. This paper surveys the most relevant CAQDAS software packages comparing their features on different areas such as data management and organization, data annotation, search and query capacities, data visualization, import/export potentialities and teamwork/collaborative work features.

Abstract