For many years trajectory data have been treated as sequences of space-time points or stops and moves. However, with the explosion of the Internet of Things (IoT) and the ood of Big Data generated on the Internet, like weather channels and social network interactions, which can be used to enrich mobility data, trajectories become more and more complex, with multiple and heterogeneous data dimensions that can be integrated with trajectories. In this paper we introduce multiple aspect trajectories and we propose a robust conceptual and logical data model and a storage solution for e cient multiple aspect trajectory queries. The main strength of our data model is the combination of simplicity and expressive power to represent heterogeneous aspects, ranging from simple labels to complex objects. We evaluate the proposed model in a tourism scenario.
With the explosion of the Internet of Things (IoT) and the ood of Big Data generated on the Internet, like weather channels and social network interactions (e.g., Flickr, Facebook, Twitter, Foursquare ), it is now possible to collect huge volumes of movement data about people, animals, and objects like cars, buses, drones, etc. Sensors installed either indoor (e.g., smart homes ) or outdoor allow the collection of data about the place, like temperature, air pollution, noise, luminosity, etc, or about the object that is moving around or inside this place, like the heart rate (with a smart watch), the emotional status (with a microphone that analyses the voice intonation), blood pressure, sleeping stages, etc. By collecting all these information we have a new type of movement data, i.e., a trajectory enriched with di erent semantic aspects.
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We can observe from Figure 1 that a trajectory became a complex object with numerous data dimensions that are contextual to the movement and heterogeneous in the form, which we de ne in this paper as aspects. The more aspects we have, the more complete is the representation of the real movement of an object, and more useful and interesting information we can infer about objects and places. The challenge is how to integrate all these heterogeneous information in a single trajectory representation, and the main questions we want to answer in this paper are: (i) Is it possible to de ne a data model that is simple in structure, but generic enough to represent any aspect related to the movement, and covers a large number of applications? (ii) Is there a way to e ciently query and extract patterns from data represented in this model?
We claim that multiple aspects represent a new view over trajectories, and a new paradigm concerning mobility data. These aspects are not only simple semantic labels, but may be complex objects and/or heterogeneous information intrinsically associated to the physical traces of the moving objects.
In this discussion paper we highlight the results of a published paper [
The main strength of our conceptual model is the combination of simplicity and expressive power for representing aspects. An aspect may be related to a moving object, to the entire trajectory or any trajectory point, and may hold any type of data, ranging from simple labels to complex objects.
For the logical model, we consider a graph-based representation (the RDF
standard [
we consider NoSQL databases for e cient storage and retrieval of large
amounts of trajectory data. Our inspiration comes from the polyglot persistence
approach [
We introduce a conceptual data model for multiple aspect trajectories, which is shown in Figure 2. We start the description of the model with the new concept of aspect.
An aspect is a real world fact that is relevant for trajectory data analysis, and it is characterized by an aspect type. For instance, the aspect train belongs to an aspect type transportation mode, and an aspect rainy belongs to an aspect type weather condition. An aspect type has a set of attributes and it may also be a subtype of a more general aspect type, allowing the modeling of an aspect type subtypeOf hierarchy, like POI accommodation hotel. More formally, an aspect type is de ned as follows. aspsupertype.
De nition 1. An Aspect Type asptype = (desc, ATT, aspsupertype) is a categorization of a real-world fact with a description desc, a set of attributes ATT = fa1; a2; : : : ; azg that hold its properties, and a (possibly empty) supertype aspect
An aspect type and its attributes act as a metadata de nition for an aspect. As a consequence, an aspect is always related to at least one aspect type and its attributes. For example, given an aspect type weather condition, some of its attributes could be temperature, wind speed and climate. In the following, we de ne an aspect.
De nition 2. An Aspect asp = (desc, SAT) is a relevant real-world fact, where desc is the aspect description, and SAT = fat1; at2; :::; atxg is a non-empty set of aspect types, with their attributes and respective values, which the aspect may hold, being ati = (asptype k, AT Vk), ati 2 SAT , a tuple with an aspect type asptype k and a non-empty set AT Vk = fa1 : v1; a2 : v2; : : : ; an : vng of attributevalue pairs so that each pair (ai : vi) 2 AT Vk is an instantiation of a property ai of asptype k with a (atomic or multivalued) value vi.
An aspect de nition supports numbers, ranges, text, geometries (when an aspect describes, for example, the shape of a hurricane at a speci c time instant), or any type of complex object.
De nition 3. A Semantic Meaning SM = (asp, asptype) is an association between an aspect asp and an aspect type asptype that gives the context of the aspect, so that asptype belongs to the aspect types of the aspect asp.
An aspect with a semantic meaning can be associated to a multiple aspect trajectory, a trajectory point, a moving object, or a relationship between moving objects in our conceptual model (see Figure 2). When an aspect varies frequently during the object movement, the aspect with its semantic meaning is associated to each trajectory point and it is called volatile aspect (VA). Some examples are the visited places (or stops) and the heart rate. An aspect is also associated to a point when it represents a sparse and instant happening, like a social media post or check-in. When an aspect does not change during an entire trajectory, it is called a long term aspect (LTA) and is associated to the multiple aspect trajectory. Examples of this kind of aspect are the town on which the trajectory occurs or the person occupation. When an aspect holds during the entire life of an object, it is called a permanent aspect (PA) and is associated to the object and not to the trajectory. One example is the person birthplace. These aspect categories are directly related to the query performance. Queries on volatile aspects, i.e., queries related to trajectory points, will be more time consuming, while long term and permanent aspects will be retrieved more quickly.
Based on these foundations, we now de ne a multiple aspect trajectory. De nition 4. A Multiple Aspect Trajectory mat = (P, S LTA, mo, desc) is a sequence of points P = hp1; p2; : : : ; pni of a moving object mo, a (possible empty) set of long term aspects S LTA, being S LTA = fsm1; sm2; :::; smpg a set of semantic meanings, and a description desc, with pi = (xi; yi; ti; S VA), pi 2 P, being x and y the spatial position of mo at the time instant t, and S VA the set of volatile aspects related to pi, where S VA = fsm1; sm2; :::; smqg is a set of (possible empty) semantic meanings.
A multiple aspect trajectory belongs to a moving object. A moving object is any entity that moves along space and time. This object is always associated to a type, which can be a person, a drone, an animal, a car, or even a natural phenomenon, like a hurricane. We formally de ne it in the following. De nition 5. A Moving Object mo = (motype, desc, S PA) is an entity that can physically move in space and time, having a description desc, a set of (possible empty) permanent aspects S PA, being S PA = fsm1; sm2; :::; smrg a set of semantic meanings, and a type motype that categorizes it.
A new feature in MASTER when compared to the state-of-the-art data models for trajectories is the moving object relationship. A moving object may hold any type of relationship with other objects, and these relationships may also be characterized by di erent aspects such as the type of relationship (e.g., friendship, professional, family ). We de ne a moving object relationship in De nition 6.
De nition 6. A Moving Object Relationship mor = (mo1, mo2, S RA) is a relevant association between two moving objects mo1 and mo2 that holds a (possible empty) set of relationship aspects S RA, being S RA = fsm1; sm2; :::; smsg a set of semantic meanings.
Finally, we model spatial features and events. The rst one denotes any relevant POI that is not spatially related to trajectory points, so it is not an aspect. Instead, it means any POI located in the trajectory neighborhood, like a nearby restaurant. In Figure 1, an example of spatial feature is the church located between the POIs work and restaurant. So, when a trajectory point intersects a relevant POI, it is modeled as an aspect. Otherwise, it is a spatial feature. Spatial features are useful for answering spatial queries like which are the restaurants located at a distance less than from the trajectory of object A? or which trajectories have an envelope whose area is higher than avenue B? Similarly, an event denotes a happening that does not have a relationship with trajectories, but it is relevant for queries that investigate events in the trajectory neighborhood. An event occurs at a spatial feature and is valid for the period that it happened.
For de ning the MASTER logical model we adopt the Resource
Description Framework (RDF) [
Figure 3 shows the proposed logical model, where dotted arrows represent an entity-attribute relationship, continuous arrows represent relationships between entities, and the ellipsis represent entities or attributes. A predicate label followed by a cardinality pair denotes a multivalued relationship. One example is a point that may be enriched with zero to several semantic meanings. An RDF triple schema in such a modeling is represented by two ellipsis connected by an arrow. One example is a moving object (subject) that is the owner (predicate) of a multiple aspect trajectory (object).
The conversion of the conceptual model to a logical schema in RDF was
inspired by several related approaches [
There is only one rule for the mapping of entities and attributes, so their conversion is straightforward. Even entities without attributes, like MovingObjectRelationship (see Figure 2), became nodes in the logical model because they have relationships with other entities.
We decided to consider the conversion to an edge if the relationship has no properties related to it, and the conversion to a node otherwise. On doing so, we avoid the generation of too many nodes in the RDF schema, and only the relationships that hold semantic meaning and hasValue as properties (see Figure 2) were mapped to nodes, and the second one was renamed to Value for sake of understanding. We also decide to maintain only the connections of Aspect and Attribute with the Value node to avoid a redundant edge between Aspect and Attribute.
The adopted storage solution for maintaining data represented in the
MASTER logical model. This solution is called Rendezvous [
The MASTER model has been evaluated over two perspectives: (i) a qualitative
analysis at the conceptual level by modeling a tourism application, as well as an
evaluation at the logical level to attest that an RDF-based storage strategy is
suitable to answer the main types of multiple aspect trajectory queries; and (ii)
a quantitative evaluation at the storage level by comparing the query running
time performance of our storage solution with a baseline. With this experiment
we show the feasibility of MASTER as a data model that can be e ciently stored
and accessed. All details are presented in the full paper [
We claim that three main types of queries can be posed to multiple aspect trajectories: (i) queries that return moving objects (e.g., which are the moving objects that were born in Florianopolis and are male? ); (ii) queries that return trajectories (e.g., which are the trajectories that stayed at an accommodation place? ); and (iii) queries that return aspects (e.g., which accommodations were visited in Paris by persons that were born in Florianopolis and are male? ). These three queries are examples of star-shaped, chain-shaped and complex queries, respectively.
Table 1 shows these queries written in SPARQL or GeoSPARQL with an arbitrary complexity depending on the number of entities that must be considered to generate the query result, which allows an e cient processing by our RDF storage solution.
Future works include a performance evaluation over larger data sets of enriched trajectories, as well as the evaluation of other Big Data storage technologies, such as NewSQL databases, for maintaining multiple aspect trajectories. We also intend to extend MASTER to model data analytics information over multiple aspect trajectories, considering, for instance, dependencies among aspects.
Although out of the scope of this paper, it is also very important to consider privacy issues that these kinds of enriched trajectories might pose. When combining the di erent semantic aspects to the location information, privacy breach might happen. It is therefore crucial to develop privacy preserving methods to guarantee privacy when multiple aspects are involved.
This work was supported by the Brazilian agencies CAPES and CNPq, and the MASTER Project which received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklowdoska-Curie agreement N. 777695. SELECT mo? SELECT t? SELECT a? WHERE f WHERE f WHERE f mo? is-a 'Person' t? composedOf p? a? rdfs:type 'Aspect' mo? enrichedWith sm? p? enrichedWith sm? a? is-a at? sm? aspect 'Florianopolis' sm? aspectType at? at? subtypeOf 'Accommodation' sm? aspectType 'birthplace' at? subtypeOf sm? aspectType a? mo? enrichedWith sm2? 'Accommodation'g p? enrichedWith sm? sm2? aspect 'Male' FILTER( sm2? aspectType 'Gender'g geof:sfIntersects(?p, Paris)) t? composedOf p? mo? owner t? mo? is-a 'Person' mo? enrichedWith sm? sm? aspect 'Florianopolis' sm? aspectType 'birthplace' mo? enrichedWith sm2? sm2? aspect 'Male' sm2? aspectType 'Gender'g (a) Star (b) Chain (c) Complex