The advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles, and animals. Many techniques have been proposed for processing, managing, and mining trajectory data in the past decade, fostering a broad range of applications. In this article, we conduct a systematic survey on the major research into trajectory data mining, providing a panorama of the field as well as the scope of its research topics. Following a road map from the derivation of trajectory data, to trajectory data preprocessing, to trajectory data management, and to a variety of mining tasks (such as trajectory pattern mining, outlier detection, and trajectory classification), the survey explores the connections, correlations, and differences among these existing techniques. This survey also introduces the methods that transform trajectories into other data formats, such as graphs, matrices, and tensors, to which more data mining and machine learning techniques can be applied. Finally, some public trajectory datasets are presented. This survey can help shape the field of trajectory data mining, providing a quick understanding of this field to the community.

/pdf/trajectory-data-mining-an-overview-3192m0wjto.pdf

Trajectory Data Mining: An Overview

This paper tackles a major privacy concern in current location-based services where users have to continuously report their locations to the database server in order to obtain the service. For example, a user asking about the nearest gas station has to report her exact location. With untrusted servers, reporting the location information may lead to several privacy threats. In this paper, we present Casper1; a new framework in which mobile and stationary users can entertain location-based services without revealing their location information. Casper consists of two main components, the location anonymizer and the privacy-aware query processor. The location anonymizer blurs the users' exact location information into cloaked spatial regions based on user-specified privacy requirements. The privacy-aware query processor is embedded inside the location-based database server in order to deal with the cloaked spatial areas rather than the exact location information. Experimental results show that Casper achieves high quality location-based services while providing anonymity for both data and queries.

/pdf/the-new-casper-query-processing-for-location-services-1kit6ixqvn.pdf

The new Casper: query processing for location services without compromising privacy

Analysis of trajectory data is the key to a growing number of applications aiming at global understanding and management of complex phenomena that involve moving objects (e.g. worldwide courier distribution, city traffic management, bird migration monitoring). Current DBMS support for such data is limited to the ability to store and query raw movement (i.e. the spatio-temporal position of an object). This paper explores how conceptual modeling could provide applications with direct support of trajectories (i.e. movement data that is structured into countable semantic units) as a first class concept. A specific concern is to allow enriching trajectories with semantic annotations allowing users to attach semantic data to specific parts of the trajectory. Building on a preliminary requirement analysis and an application example, the paper proposes two modeling approaches, one based on a design pattern, the other based on dedicated data types, and illustrates their differences in terms of implementation in an extended-relational context.

/pdf/a-conceptual-view-on-trajectories-k8d3970n1n.pdf

A conceptual view on trajectories

The collection of moving object data is becoming more and more common, and therefore there is an increasing need for the efficient analysis and knowledge extraction of these data in different application domains. Trajectory data are normally available as sample points, and do not carry semantic information, which is of fundamental importance for the comprehension of these data. Therefore, the analysis of trajectory data becomes expensive from a computational point of view and complex from a user's perspective. Enriching trajectories with semantic geographical information may simplify queries, analysis, and mining of moving object data. In this paper we propose a data preprocessing model to add semantic information to trajectories in order to facilitate trajectory data analysis in different application domains. The model is generic enough to represent the important parts of trajectories that are relevant to the application, not being restricted to one specific application. We present an algorithm to compute the important parts and show that the query complexity for the semantic analysis of trajectories will be significantly reduced with the proposed model.

/pdf/a-model-for-enriching-trajectories-with-semantic-1s00xjclgc.pdf

A model for enriching trajectories with semantic geographical information

Nearest neighbor selection for iteratively kNN imputation

This document presents a method to design scalable and representative moving object data (MOD) and two sets of queries for benchmarking spatio-temporal DBMS. Instead of programming a dedicated generator software, we use the existing Secondo DBMS to create benchmark data. The benchmark is based on a simulation scenario, where the positions of a sample of vehicles are observed for an arbitrary period of time within the street network of Berlin. We demonstrate the data generator's extensibility by showing how to achieve more natural movement generation patterns, and how to disturb the vehicles' positions to create noisy data. As an application and for reference, we also present first benchmarking results for the Secondo DBMS. Whereas the benchmark focuses on range queries, we demonstrate its ability to incorporate new future classes of queries by presenting a preliminary extension handling various nearest neighbour queries. Such a benchmark is useful in several ways: It provides well-defined data sets and queries for experimental evaluations; it simplifies experimental repeatability; it emphasizes the development of complete systems; it points out weaknesses in existing systems motivating further research. Moreover, the BerlinMOD benchmark allows one to compare different representations of the same moving objects.

BerlinMOD: a benchmark for moving object databases

Databases supporting time dependent and continuously changing geometries, called
moving ob-
jects databases
, have been studied for about 15 years. The field has been flourishing and there exist
many hundreds, more likely thousands, of publications. However, very few of these results have
made it into systems (research prototypes or commercial) and are available for practical use today.
It is not that the publications are purely theoretical. In most cases data structures and algorithms
have been proposed, implemented, and experimentally evaluated. However, whereas there exists a
well established infrastructure for publishing research papers through journals and conferences, no
such facilities exist for the publication of the related prototypical implementations. Hence imple-
mentations are just done for experiments in the paper and then usually abandoned. This is highly
unfortunate even for research, as future proposals of improved algorithms most often have to reim-
plement the previous techniques they need to compare to.
In this paper we describe an infrastructure for research in moving objects databases that addresses
some of these problems. Essentially it allows researchers to implement their new techniques within
a system context and to make them available for practical use to all readers of their papers and
users of the system. The infrastructure consists of
S
ECONDO
, an extensible database system into
which a lot of moving object technology has been built already. It offers BerlinMOD, a benchmark
to generate large sets of realistic moving object trajectories together with a comprehensive set of
queries. Finally, it offers
S
ECONDO
Plugins as a facility to publish new research implementations
that anyone can merge with a standard
S
ECONDO
distribution to have them run in a complete system
context.

/pdf/secondo-a-platform-for-moving-objects-database-research-and-3vp1x6izbk.pdf

SECONDO: a platform for moving objects database research and for publishing and integrating research implementations

The goal of SECONDO is to provide a "generic" database system frame that can be filled with implementations of various DBMS data models. SECONDO was intended originally as a platform for implementing and experimenting with new kinds of data models, especially to support spatial, spatio-temporal, and graph database models. We now feel, SECONDO has a clean architecture, and it strike a reasonable balance between simplicity and sophistication. Since all the source code is accessible and to a large extent comprehensible for students, we believe it is also an excellent tool for teaching database architecture and implementation concepts. SECONDO runs on Windows, Linux, and Solaris platforms, and consists of three major components SECONDO kernel, optimizer, and graphical user interface.

SECONDO: an extensible DBMS platform for research prototyping and teaching

Representing descriptions of movements in databases and querying them is a basic capability required in mobile data management. In this demonstration, we show for the first time a prototype implementing a data model and query language for moving objects (trajectories) completely integrated into a DBMS environment, including query optimization and user interface issues such as animation.

Querying Moving Objects in SECONDO

We describe 
S
ECONDO
, an extensible DBMS platform suitabl
e for building research prototypes and
for teaching architecture and impl
ementation of database systems. It does not have a fixed data
model, but is open for im
plementation of new models. 
S
ECONDO
 consists of three major components
which can be used together or independently: (i) 
the kernel, which offers query processing over a set
of implemented algebras, each offering some type 
constructors and operators, (ii) the optimizer,
which implements the essential part of an SQL-lik
e language, and (iii) the graphical user interface
which is extensible by viewers fo
r new data types and which provides a sophisticated viewer for spa-
tial and spatio-temporal (moving) objects.
 Examples of algebras implemented in 
S
ECONDO
 are rela-
tions, spatial data types, R-trees, or midi object
s (music files), each with
 suitable operations. The
kernel is extensible by algebras, the optimizer 
by optimization rules and cost functions, and the GUI
by viewers and display functions.
A highlight of the description is a new algor
ithm for conjunctive query optimization which is
remarkably simple, yet performs very well. We al
so emphasize a technique for selectivity estimation
suitable for an extensible envi
ronment with complex algebras for non-standard data types.

SECONDO: an extensible DBMS architecture and prototype

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