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In ubiquitous computing we need to know the present context in order to interact properly with the nearby smart elements. When we are moving outdoors, mobile devices take a very important role because they provide us with a link between the world outside and ourselves through means of intelligent interfaces. There are a lot of situations in which it would be very useful to know or foresee the future context, i.e. as a geographic environment, where we could find ourselves in a near future, and at the same time being able to use that information from our devices. Therefore we must preview this location with enough precision and time and be able to use this information from our mobile device. In our “OnTheWay” system, we used GPS technology and databases made of past paths taken by a person, in order to predict the next location, once we had begun a new course, comparing the new one with those ones stored. The results were amazing: from the data collected about paths travelled during a month and five days, we got the actual destination in 98% of cases, when we have only made a 30,35% of the total path. Therefore, including statistic and semantic information will allow us to upgrade our results, due to the sedentary human behaviour, the small number of frequently visited locations and the fact that the paths used to arrive to these locations are usually the same.
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Stored only when both papers are in the corpus — 3 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.