Francisco Moreno

Leadership groups on Social Network Sites based on personalized PageRank

In this paper we present a new framework to identify leaders on an SNS using the Personalized PageRank vector. The methodology is based in the concept of Leadership group recently introduced by one of the authors. We show how to analyze the structure of the Leadership group as a function of a single parameter. Zachary’s network and a Facebook university network are used to illustrate the applicability of the model. As an application we introduce some new concepts such as the probability to be a leader, a classication of networks and the concept of best potential friend.

Extending the map cube operator with multiple spatial aggregate functions and map overlay

The map cube operator for spatial multidimensional databases supports spatial aggregation using geometric union and enables information visualization in maps. In this paper, we propose three extensions to the map cube operator. First, support for spatial aggregate functions other than geometric union. Second, support for several spatial aggregate functions simultaneously. Third, support for overlaying the map cube results with maps. In addition, we fix some grammar inconsistencies of this operator. We illustrate our proposed extensions and improvements in a case study about crimes.

NewFriends: an algorithm for computing the minimum number of friends required by a user to get the highest PageRank in a social network

The online social networking phenomenon is growing rapidly all around the world. As a consequence, in recent years, several studies have been devoted to the analysis of social network sites. A specific issue that has been addressed is the identification of leaders of the social network based on well-known algorithms such as PageRank. In this paper, we propose a novel algorithm based on the PageRank method to determine the minimum number of new friends required by a user of a social network to become the user with the highest PageRank in the network. We provide formal mathematical definitions and validate our proposal with some experiments based on artificial and real data.

Season queries on a temporal multidimensional model for OLAP

Dimensions are usually considered static in a data warehouse. However, because of changing requirements, dimension data and dimension structure can evolve. In this paper we focus on a type of dimension data change called reclassification, i.e., when a member of a level changes its parent in a higher level of a dimension. This kind of change gives rise to the notion of season, i.e., an interval during which two members of a dimension are associated with each other. In this paper we extend a formal temporal multidimensional model with the notion of season and propose query language constructs to enable season queries. A case study about soccer illustrates the application of the proposed extensions, exemplified with several season queries.

Using Criteria Reconstruction for Low-sampling Trajectories as a Tool for Analytics

Abstract Mobile applications equipped with Global Positioning Systems have generated a huge quantity of location data with sampling uncertainty that needs to be handled and analyzed. Those location data can be ordered in time to represent trajectories of moving objects. The data warehouse approach based on spatio-temporal data can help on the analysis. For this reason, we consider the problem of personalized reconstruction of low-sampling trajectories and include the criteria of movement as a dimension of analysis in a trajectory data warehouse. We enhance the analytics using dimensional modelling and graphical analysis in order to provide mechanisms to help decision makers. For example, analysts may formulate queries such as What are the top 5 most traversed streets between 07:00:00 am and 09:00:00 pm on August 9, 2014 (Saturday) if the trajectories are reconstructed using the touristic criterion? The answer to this query may help users to identify, e.g., city bottlenecks.

My Best Current Friend in a Social Network

Abstract Due to its popularity, social networks (SNs) have been subject to different analyses. A research field in this area is the identification of several types of users and groups. To make the identification process easier, a SN is usually represented through a graph. Usual tools to analyze a graph are the centrality measures, which identify the most important vertices. One of these measures is the PageRank (a measure originally designed to classify web pages). Informally, in the context of a SN, the PageRank of a user i represents the probability that another user of the SN is seeing the page of i after a considerable time of navigation in the SN. In this paper, we define a new type of user in a SN: the best current friend. Informally, the idea is to identify, among the friends of a user i , who is the friend k that would generate the highest decrease in the PageRank of i if k stops being his/her friend. This may be useful to identify the users/customers whose friendship/relationship should be a priority to keep.

Trajectory reconstruction using personalized routing over a graph

Location based services provide information enriched with geo-located data. However, the detailed trajectory of a moving object is lost when low-sampling data are present. Previous works have been developed to explore trajectories using location history logged by users in order to reconstruct the trajectory of a moving object. Those methods can be considered as reconstruction or imputation processes. In this paper, we reconstruct trajectories using the personalization features of routing theory based on criterion decision and over a graph. In addition, we consider the trajectory reconstruction only in a constrained environment, a road network.

An Analysis of a Methodology that Transforms the Entity-Relationship Model into a Conceptual Model for a Graph Database

The graph databases (GDB) have gained a lot of importance in the last years; this is due to the necessity to store and manage very large volumes of data whose natural structure is a graph. However, nowadays there do not exist conceptual models widely accepted to represent a GDB. This fact implies that the analysts are guided considering their experience and best practices. There have been proposed different conceptual models for GDB; in this paper, we analyze a methodology that generates a conceptual model for a GDB from the entity-relationship (E-R) model. We explore several limitations of this methodology and offer some ideas for solving them.

A Trajectory Model to Deal with Transmission Failures

A trajectory records the evolution of the position of a moving object in a space during a time interval. In Spaccapietras trajectory model, trajectories are segmented in subintervals called stops and moves. On the other hand, during some periods failures can occur in the transmission of data of the trajectory causing missings of information. In this paper, we extend Spaccapietras model by incorporating the missing information as a component of a trajectory. We consider this issue not only with regard to the object position but also with regard to other attributes of the trajectory (complementary attributes). We propose a classification for these attributes, depending on whether they are constant or variable during the stops and the moves. We also propose two algorithms: i) to convert a sequence of observations of a trajectory into stops, moves and missings. ii) to check that the data recorded for the attributes whose value must be constant is consistent.

A spatio-temporal extension to the map cube operator

OLAP (On Line Analytical Processing) is a set of techniques and operators to facilitate the data analysis usually stored in a data warehouse. In this paper, we extend the functionality of an OLAP operator known as Map Cube with the definition and incorporation of a function that allows the formulation of spatio-temporal queries. For example, consider a data warehouse about crimes that includes data about the places where the crimes were committed. Suppose we want to find and visualize the trajectory (a trajectory is just the path that an object follows through space as a function of time) of the crimes of a suspect beginning with his oldest crime and ending with his most recent one. In order to meet this requirement, we extend the Map Cube operator.