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applied over text corpora [3] and for classification tasks on video sequences [4]. In
this work, the LDA model is applied to sequences of occupancy logs provided by
a sensor network in a modern oce building. The main goal is to automatically
discover long-term-behavior patterns or routines on the logged data. One of the
main issues of this work, compared to other approaches, is that we only employ
occupancy sensors that provided binary information, indicating if the room is
occupied or not. Thus, it is a technique which could be easily implemented and
deployed and also respect privacy issues.
This paper covers the completely process under two different phases. First,
it is necessary to obtain a good representation of the long-term activity of an
specific room. Secondly, in a learning phase we answer the question of how to
find the most common patterns given the training data.
In the next section some related works are described. Then, section 3 illus-
trates how to build the representation of the long-term activity in the sensor
network. Section 4 focused on how to generate the LDA model and review it.
The results of some experiments are shown in section 5. Finally, section 6 con-
cludes the paper.
2 Related Works
With the recent advances on sensor networks it becomes more easy to ob-
tain and process the provided data. There are a lot of research on the com-
puter vision community which aims to model the behavior of the people being
monitored. However, that research is outside of the scope of this work since
we are not dealing with visual sensors. Similar approaches in the literature
which covers occupancy data in an oce environment could be found in the
works employing the Mitsubishi Electric Research Lab (MERL) Dataset [5].
MERL dataset is a public dataset which provides around 30 million of sen-
sor logs from a network of 200 sensors in an oce environment. The dataset
consist of sensors readings recorded for about 1 year at MERL building. In
[6], the authors use the MERL dataset with the the aim of modeling three
different social features: (1) visiting another person, (2) attending meetings
with another person and (3) traveling with another person. They employed
information theoretic measures (entropy) and graph cuts to obtain the pre-
vious patterns on the MERL dataset. To extract relationships on the occu-
pancy data they model pairwise statistics over the dataset. Their goal was to
identify potentially important individuals within the organization. In [7], the
authors reviewed the existing techniques for the discovery of temporal pat-
terns in sensor data and proposed a modified T-Pattern algorithm [8]. This
algorithm was tested using the MERL dataset and the presented results out-
performed the Lempel-Ziv compression based methods. The work of [9] also
introduce temporal information in the activity recognition problem, but us-
ing temporal evidence theory. Their framework was tested using a smart home
dataset.
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