Table of Contents Author Guidelines Submit a Manuscript
Mobile Information Systems
Volume 2016 (2016), Article ID 9673048, 12 pages
http://dx.doi.org/10.1155/2016/9673048
Review Article

Towards Mobile Information Systems for Indoor Space

1School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China
2Key Laboratory of Electromagnetic Space Information, Chinese Academy of Sciences, Hefei 230027, China

Received 10 December 2015; Accepted 17 February 2016

Academic Editor: Miltiadis D. Lytras

Copyright © 2016 Xiaoxiang Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

With the rapid development of Internet of things (IOT) and indoor positioning technologies such as Wi-Fi and RFID, indoor mobile information systems have become a new research hotspot. Based on the unique features of indoor space and urgent needs on indoor mobile applications, in this paper we analyze some key issues in indoor mobile information systems, including positioning technologies in indoor environments, representation models for indoor spaces, query processing techniques for indoor moving objects, and index structures for indoor mobile applications. Then, we present an indoor mobile information management system named IndoorDB. Finally, we give some future research topics about indoor mobile information systems.

1. Introduction

Over the past years, with the rapid development of indoor localization technologies, such as Wi-Fi and RFID, mobile information systems for indoor space have emerged as a hot research topic. Indoor space has some unique features, which calls for new techniques to develop mobile information systems towards indoor space.

Comparing with Euclidean space and road network space, moving objects in indoor space have several unique features:

(a) Indoor space is a limited three-dimensional space. The movement of objects in indoor space is limited by walls, doors, and obstacles. Some other restrictions on doors may also exist. For example, one-way doors can only be accessed during a specific time frame, for example, from 8 AM to 5 PM. Such constraints combining temporal and spatial properties bring new challenges to modeling and querying indoor moving objects.

(b) Both Euclidean space and road network space use GPS (Global Positioning System) to get their latitude and longitude coordinates, which is not suitable for indoor space. New positioning techniques such as Wi-Fi, RFID, and Bluetooth are employed for the localization in indoor space. In addition, instead of geographic coordinates, symbolic coordinates like “Floor 5, Room 503” are usually used in indoor environments. The change of positioning technologies and coordinate systems calls for redesigns of moving object databases for indoor space.

(c) In outdoor space, distance between two objects is computed by their latitude and longitude coordinates. However, in indoor space, a new distance measurement method needs to be defined based on symbolic coordinates, which has to consider the influence of indoor elements like elevators, stairs, and obstacles. Distance-aware queries (such as NN queries and navigation queries) in outdoor space need to be adjusted to the new distance measurement in indoor space.

These new indoor-space features make it difficult to use existing technologies in outdoor mobile data management for indoor space. On the other side, there are many potential applications in indoor space, which are summarized as follows.

(a) Indoor Navigation. Since indoor environment is a limited space with a series of constraints and obstacles, a navigation system is needed for guiding users to their destinations. For example, an airport navigation system on mobile devices can help foreign tourists find an optimized path of shopping, waiting, and boarding. An indoor park guiding system can help drivers find their way to an empty parking space or the exit [1].

(b) Information Acquisition and Recommendation. Information acquisition and recommendation are an important component of indoor location-based services. It allows users to have an interaction with nearby interest points or other users. In a large shopping mall, customers can use such information system on their mobile terminals to find stores which are conforming to their interest or have a high score. On the business side, they can push ads or discount messages to customers based on their location and interest.

(c) Objects Monitoring and Management. In some situations, applications of monitoring indoor objects are in demand. These situations include products on a pipeline, stocks in a warehouse, and equipment in a hospital. A well-built indoor object monitoring system can help administrators in control of these objects’ movement and they can be informed immediately when error occurs.

(d) Outliers Detection. Outliers can be detected from a large number of indoor moving objects by analyzing their trajectories [2, 3]. This technology can be helpful in the field of public security. People with an abnormal behavior pattern can be seen as a threat in some specific indoor space, such as metro and museum.

(e) Indoor Social Network Service. So far, SNS (Social Network Service) has become one of the most popular forms of communication on network. Some SNS applications like Foursquare are famous for their location-based service. Users can “check in” their current “venue” and share it to friends. However, GPS based localization technology cannot recognize people’s indoor position precisely, so we need indoor information systems to improve the user experience in SNS [4, 5].

Since there are many kinds of new applications in indoor space and previous knowledge in Euclidean space or road network space cannot support indoor space, new designs have to be considered towards indoor mobile information systems. Recently, the research is just beginning and in rapid developing stage now.

The first light of research on indoor mobile information system appears in 1990s, which was focused on indoor robot navigation system in the beginning. Surmann et al. [6] designed a fuzzy indoor mobile robot navigation system with the technology of artificial intelligence. Later, several indoor navigation systems were implemented by using kinds of physical sensors [7, 8]. But lacking effective way to acquire the precise position of indoor objects makes these systems hardly practical. In the last decade, with the development of indoor localization technology and smart mobile devices, researchers gradually began to pay attention to the management of indoor moving objects. Jensen et al. started to work on indoor moving objects database in 2009; his first research included modeling indoor space and analyzing uncertainty of indoor moving objects [9]. After Jensen’s step, Baniukevic et al. in Aalborg University, Denmark, have got a series of achievements in this field such as improving indoor positioning [10], indoor query processing [11, 12], and indoor-space indexing [13]. Worboys in University of Greenwich mainly pays attention to modeling indoor space [14, 15] and the correlation between indoor and outdoor [16, 17].

Although there have been many researches on indoor mobile information systems, a large number of them are aimed at implementing an indoor positioning or navigation system. Research on modeling, querying, and indexing for indoor mobile information systems is still in an early stage. Some complex problems are urgently still to be considered.

In this paper, we summarize the features of indoor mobile information systems and give a survey on several key technologies which support indoor data management. Then, we introduce our prototype system that is called IndoorDB. Finally, we propose several future research topics in indoor mobile information systems. Briefly, we make the following contributions in this paper:(a)We make a survey on the key issues in indoor mobile information systems. We analyze the special features of indoor spaces and summarize the recent advances in this area.(b)We present a preliminary indoor mobile information system which is called IndoorDB. It integrates a number of technologies in indoor-space-related studies such as indoor positioning techniques, indoor models, and indoor query processing. We describe the data model as well as the implemental details of IndoorDB. To the best of our knowledge, this is the first prototype system in the area of indoor mobile information systems.(c)We propose some future research topics for indoor mobile information systems, including privacy protection in indoor spaces, indoor trajectory analysis, and integration of indoor and outdoor spaces.

The rest of this paper is organized as follows. Section 2 reviews the key issues of indoor mobile information systems. Section 3 presents the design and implementation of our indoor mobile information system IndoorDB. Section 4 gives a glimpse of future research directions and Section 5 concludes the paper.

2. Key Issues in Indoor Mobile Information Systems

2.1. Indoor Positioning Technologies

Indoor positioning technology is a hot topic in past several years. Since GPS cannot be deployed for indoor use, researchers have come out with dozens of indoor positioning technologies. Each technology derives many prototype positioning systems. There have been already several surveys about indoor positioning technologies and systems since 2007 [1821]. Among these positioning technologies, a constant theme is the trade of accuracy and cost. This cost includes the spending of infrastructure, the time complexity of positioning algorithm, the durability of batteries, and the usability. For example, [22] proposed an infrastructureless indoor positioning system by taking advantage of sensors in smartphone. However, the accuracy of the system was sacrificed in large indoor space. In contrast, a Bluetooth-based positioning system implemented in [23] has an accuracy of 1–3 meters, but the cost of setting up Bluetooth base stations cannot be ignored.

Usually, indoor mobile information systems need an accuracy of a few meters, low power consumption, and a short response time. And indoor spaces often have a range of dozens to hundreds of square meters so that the cost of the infrastructure construction must not be too high. According to these features in indoor space, the most suitable and also the most widely used positioning technology is RSSI (Received Signal Strength Indicators). Wireless radio waves can pass through walls and human bodies so that the positioning system has a larger coverage area and fewer infrastructures than other systems. WLAN and RFID are two typical positioning systems making use of RSSI method.

2.1.1. WLAN

WLAN (Wireless Local Area Networks, IEEE 802.11 standard) is the most popular positioning method today. Since WLAN has become the most common way to connect to internet from a wireless device, many indoor environments already provide a deployment of WLAN infrastructures, which lower the cost of indoor positioning. The performance of WLAN positioning, like accuracy or consumption, is able to satisfy the demand of indoor mobile systems and can be improved by using other existing sensors (gravity and inertial sensor, etc.) in users’ mobile devices.

There are some different positioning strategies in WLAN positioning method. Empirical fingerprinting approach needs to measure and store a “fingerprint map” of radio signal strength offline first. When a new RSS measurement comes, compare it with signal strength in “fingerprint map” and locate it to a position in the map with a nearest signal distance. In [29], the authors proposed an effective approach to measuring the map, while, in [30, 31], the authors considered the changes of environment and provided a way to adjust it. Already some commercial fingerprinting systems have been implemented. Gallagher et al. [32, 33] proposed a commercially available location system with an average accuracy of 7 m for indoor environment. Reference [34] provided a fusion system of WLAN, sensors in mobile phone, and landmarks. Sensors and landmarks are used to modify existing fingerprinting, and finally they achieved a mean localization accuracy of 1 m. Reference [35] discussed the applicability of multiwall multifloor propagation models to fingerprinting technology and proved that the model can be a promising solution.

2.1.2. RFID

RFID (Radio Frequency Identification) is a system including a number of RFID readers, RFID tags, and the communication system between them. In the system, data can be transmitted from tags to the readers via radio waves within the valid range. When an RFID tag moves into the dominating area of a certain reader, the signal received by the reader can help system locate the position of tag. According to the principle of RFID system, the accuracy is depending on the density of reader deployment and reading ranges. It means that RFID systems usually have a higher accuracy as well as a higher cost in comparison with WLAN technology.

RFID systems can be categorized as passive or active by the transmit type of RFID tags. Passive RFID tags work without a battery. They receive the signals from readers passively and reflect signals after modulating. But the ranges of this system are very limited; also the readers can be very expensive. Researches have shown the ability of passive systems in some field such as vehicle guidance [36] and inventory control [37]. Active RFID tags equipped with internal battery power can actively transmit their ID or other data to readers. It enables a long detection range of 10–30 m. So active RFID can be well used for indoor positioning, which has been shown in recent research [3840]. Also, there have been some systems that use a hybrid of WLAN and RFID technology like [41].

2.2. Modeling Indoor Space

As discussed in Section 1, indoor space usually has some unique properties. With the growth of indoor mobile information systems, there is a need for modeling indoor space to find out how to represent the features of indoor space appropriately and properly [42]. We will talk about 4 kinds of existing indoor-space models in this section: object feature model, geometric model, symbolic model, and hybrid model.

(a) Object feature model represents the features of indoor space and the relationship between operations and types. CityGML/IndoorML [43] is a UML- (Unified Modeling Language-) based class model. IndoorML categorizes indoor element into subspace, wall, door, floor, moving object, and so forth and shows semantic information and spatial topology relations by the use of UML class diagram. Object feature model has a good expansibility but cannot express the geometrical feature of indoor elements and cannot support indoor distance-aware queries either.

(b) Geometric model focuses on the geometric representation of indoor space, which is mainly used in visualization, indoor navigation, or computer aided design (CAD). Raster model is a kind of typical geometric model, which divides the indoor space into a numbers of regions without overlap. There is a benefit that the relationship of adjacency can be inferred from other regions implicitly. The regions can be regular shapes like square [44]. Also regions can be divided irregularly into triangle [45], quadrilateral [46, 47], or Voronoi graph [48]. Geometric model can effectively support the representation of location and direction and the calculation of indoor distance. However, geometric model is lacking in the representation of connectivity; it is not helpful in indoor navigation queries.

(c) Symbolic model is the most widely used model in indoor space so far. In this model, each indoor element is given a symbolic ID. The relationship within symbolic entities in symbolic space can express the topological relation in indoor space. The representations of symbolic space can be classified into set-based, topology-based, lattice-based, and graph-based. Becker and Dürr [49] presented a set-based model to express indoor location information. This model can improve range query by making use of set operations but cannot support connectivity related queries. Ben et al. [50] presented a semantic-based model for indoor space and moving object. Li and Lee presented a topology-based semantic model in 2008 [51] and a lattice-based semantic location model [52]. Graph-based model is the most popular symbolic model. Jensen et al. presented a deployment graph model in 2009 [5355] to support tracking and monitoring moving objects in an RFID positioning system. The door graph model proposed in [12] is good at dealing with indoor distance query like NN query. Lu et al. presented an extended graph model in 2012 [11], which can handle several kinds of indoor distance-aware queries. These models take advantage of the representation of indoor connectivity but often focused on only one certain area. At the same time, symbolic model can hardly support indoor geometric features, so it is hard for symbolic model to work out indoor uncertainty query.

(d) Since symbolic model and geometric model both have a limitation of representing indoor space, there have been some hybrid models to improve them. The 2D-3D hybrid model proposed by Kim et al. [56] uses a 2D floor layer as data structure and stores 3D visualization data with 2D symbolic data. It can support both the visualization of indoor space and the navigation in indoor space. Li et al. presented a grid graph-based hybrid model in 2010 [57]. The model is a combination of grid representation and gird graph. Grid-based model can express the geometric information in indoor space and a gird graph is used to show the connectivity among grids. Jamali et al. proposed an automated 3D indoor topological modeling [58]. The model includes 3D building modeling and topological navigation networking, which makes the indoor space visually without losing connectivity information.

2.3. Queries on Indoor Moving Objects

According to the application area of indoor mobile information system, indoor application service can be divided into two categories: individual service and public query. Individual service provides users with not only basic location service but also navigation, recommendation, SNS service, and so on. On the other hand, public service can help administrator with indoor monitoring, behavior prediction, outlier detection, public safety management, and other public requests. The query type for these two kinds of service is different. The classification of these query types is shown in Figure 1.

Figure 1: Classification of indoor queries.
2.3.1. Indoor Individual Service Query

(1) Location Dependent Query. Location dependent query is a basic query in indoor space. Since indoor positioning technology cannot provide the locations of moving objects continuously, for each location dependent query or other location-based query, it should be a query for moving object’s location first. Moving object tracking query [59] and indoor-space membership query [60] are two kinds of typical location dependent query. The former tracks one or some moving objects for a series of time, and the latter returns a result of whether moving objects are in a specified indoor area. In [61], the authors proposed a location dependent predictive query. The query extends moving object’s movement by its current location and velocity and generates a trajectory to predict its location in the future.

(2) Indoor Navigation Query. Indoor navigation query gives a start point and a destination in indoor environment and returns an optimal path from start point to destination. In [62], a basic indoor navigation query in a hybrid indoor model is discussed. In this case, the shortest path is the best path. In [63], the authors proposed a context-aware navigation query based on a multilayered indoor-space model. The path selection is influenced by semantics, so that the query can still provide best path in case of emergency. References [64, 65] also drew attention to the context in indoor navigation systems. It shows that the context includes the interest and physical condition of users, the memory and network condition of mobile devices, and the external causes like time and temperature. Reference [60] gave an example of limited indoor navigation query: users may have some additional demands when planning their way to the destination. For example, go to a shop in upper story but do not use the lift. Reference [60] arranged the limit to regular expression and put it into the query; thus, the result path can satisfy the additional demands.

(3) Indoor Distance-Aware Query. Distance-aware queries in indoor space have similar definition with outdoor space. But the differences between indoor and outdoor space models make the researches on these queries significant. Indoor range query [11, 53, 66] and indoor NN query [11, 12, 24] have attracted many researchers to work on them. According to the different temporal predicates, distance-aware queries can be categorized into snapshot queries or continuous queries. And continuous queries also have several types. Reference [53] proposed a continuous query where query point is moving and query objects are static. In indoor situation, it can be a walking user looking for a nearest staircase. Then, [66] gave another kind of continuous query where query point is static and query objects are moving. This kind of continuous query can be useful when a shop owner in a shopping mall wants to know who will come into the shop among the outside customers.

2.3.2. Indoor Public Service Query

(1) Indoor Range Monitoring Query. Indoor range monitoring query mainly includes two kinds of spatial-temporal query. One is indoor spatial-temporal range query, used for searching moving objects that stay in a certain indoor range during a period of time. The other one is indoor topological query, used for searching moving objects that enter, leave, or pass through a certain indoor range during a period of time. Both of them are aiming to monitor indoor moving objects in time and space domain. Jensen et al. designed two indexes for supporting indoor range monitoring query in 2009 [26]. Both indexes are extensions of R-tree. Reference [53] came out as a continuous spatial-temporal monitoring query, which continuously monitors the entering and the leaving of moving objects.

(2) Indoor Join Query. Reference [55] worked on indoor join operation and proposed a query named PTISSJ (Probabilistic Threshold Indoor Spatiotemporal Join). The query returns object pairs where the two objects meet in their tracking history with a probability greater than the given threshold. Since indoor positioning technology cannot get the dense trajectory data like GPS data, there is an uncertainty in indoor trajectory query. So a probabilistic threshold is given to fit indoor environment. Reference [67] kept an eye on indoor distance-aware join query. They considered both range join and neighborhood join.

(3) Indoor Trajectory Similarity Query. Traditional idea for the comparison of trajectory similarity is LCSS (Longest Common Subsequence) or ED (edit distance). But common methods are not quite suitable for indoor space. In [68], the authors designed a comparison algorithm by taking use of history trajectories of moving objects. Both Euclidean distance and edit distance are considered in the algorithm. In [69], the authors brought more information to judge the similarity of two trajectories and use the result of query to improve the performance of personalized recommendation. The added information includes visited locations, residence time, and the popularity of visited location.

2.4. Indices for Indoor Mobile Information Systems

Index is needed in indoor information systems to improve the query performance. So far, indexes for moving objects or moving objects’ trajectory are usually based on R-tree in outdoor space. There are varieties of R-tree-like indexes such as 3D R-tree [70], HR-tree [71], and TPR-tree [72]. However, indoor space has the components of rooms, doors, floor, stairs, and so forth, so these should be taken into consideration when developing an index for indoor environment. Recently, research on indoor indexes is just beginning. Most of the indexes only support a few queries based on some specific models. These indexes can be divided into two groups by the object they index: indoor-space index and indoor moving objects index.

2.4.1. Indexing Indoor Space

Reference [11] proposed an index called DPT (Door-to-Partition Table). A precalculated distance index matrix is used to represent the distances of each door pair. The DPT stores the relationship between doors and partitions of indoor space. Thus, it is easy to figure out door-to-partition distance by DPT and distance index matrix so the index can support indoor distance-aware query. Reference [24] developed a Composite Index which can adapt to the change of indoor environment. Firstly, an R-tree index is implemented based on the location of rooms, then, for each room, a hash table is built to index the moving objects in the room. The work in [25, 73] focused on connectivity query and came out as an indoor-tree index. Indoor-tree has a similar structure with Composite Index, except that the R-tree it implements is based on the connectivity of rooms.

2.4.2. Indexing Indoor Moving Objects

Based on R-tree, [26] designed two moving objects trajectory indexes RTR-tree and TP2R-tree. RTR-tree treats the trajectory as several horizontal lines, and TP2R-tree compresses the trajectory into a point with time parameters. Indoor range query and indoor trajectory query can benefit from these structures. The work in [27] added another R-tree to RTR-tree and became a new index called Dual R-tree. The purpose of the new added R-tree is to index moving objects so that the performance of trajectory query can be improved. ACII index in [28] also has a double structure: MC uses R-tree to index the indoor space and a hash table to index location of moving objects at current time; MEMO stores the history trajectory with a primary key of moving objects. With the double structure, ACII index can support full temporal moving objects query.

A summary of indoor indexes introduced above is in Table 1. According to the table, many issues need to be worked out, including the exploration of new index structure, the extension of supported query, and the implementation of useful indoor model.

Table 1: Summary of indoor indices.

3. Implementing an Indoor Mobile Information System

In this section, we present an indoor mobile information system called IndoorDB that integrates many indoor-space-based designs.

3.1. Data Model of IndoorDB

IndoorDB is implemented on the basis of an indoor moving object data model named LayeredModel [74], which is a symbolic and semantic model for indoor space as well as indoor moving objects. Formally, LayeredModel is represented as a set of 5-tuple, as shown in formula (1). Consider

Here, DL is a set of doors, RL is a set of rooms, OL is a set of objects, CE is a set of connection edges between doors and rooms, and LE is a set of location edges between objects and rooms.

Figure 2 shows an example of indoor space and Figure 3 shows the conceptual structure of the LayeredModel. Compared with common symbolic models for indoor space, LayeredModel supports richer semantics. For example, it can support navigation queries given like “Go to Room 7 from Room 3 without passing through Room 1 and Door 11.

Figure 2: An example of indoor space.
Figure 3: Conceptual structure of the LayeredModel.
3.2. Implementation of IndoorDB

IndoorDB is a LayeredModel based indoor moving object management system. It is an extension of Oracle 11g DBMS. Figure 4 shows the architecture of IndoorDB.

Figure 4: Architecture of IndoorDB.

There are two main components of IndoorDB divided by the dashed line in the figure. The lower part is the data storage and management part; LayeredModel has been implemented as the underlying indoor model of IndoorDB. The ability of IndoorDB is depending on this underlying model. Also a series of type systems and operation functions is developed to enrich LayeredModel so that the system can store and manage the data of indoor moving object. The upper part is the part for application and interaction. Oracle Maps API accesses JDBC API to get the indoor map data, and after drawing the map, Map Viewer service presents it to the user interface.

IndoorDB extends three categories of new data types into Oracle, namely, temporal data types, spatial data types, and moving objects types (as shown in Figure 5). The moving objects types contain moving base types and an indoor moving object type. The former refers to the numeric, Boolean, or string values changing with time, whereas the latter refers to the indoor moving objects as well as their trajectories. All the new data types are implemented by PL/SQL using the CREATE TYPE statement. Figure 6 shows an example of indoor moving objects and the definition of indoor moving object type in IndoorDB.

Figure 5: Type system of IndoorDB.
Figure 6: Defining the indoor moving object type in IndoorDB.

IndoorDB implements ten types of spatiotemporal operations, which are (1) object data management operations, (2) object attribute operations, (3) temporal dimension project operations, (4) value dimension project operations, (5) temporal selection operations, (6) quantification operations, (7) moving Boolean operations, (8) temporal relation operations, (9) object relation operations, and (10) distance operations. All the operations are implemented by PL/SQL and as member functions of extended data types, as shown in Figure 2. For the space limitation, we will not discuss the details about each data operation. However, in the demonstration process, we will show how to use those operations to answer different spatiotemporal queries.

The web-based client with an interactive interface is shown in Figure 7. Figure 7 also shows an instance of indoor semantic navigation query. The semantic information of each room in Figure 5 is shown in Table 2. And the semantic navigation query can be seen in the right side of Figure 7: “Include: iPhone, rest; Exclude: canon; End: tea.” The result is shown in the map as well as the lower right corner. As it can be seen, the system satisfies the constraints and returns a shortest path.

Table 2: Semantic information of rooms in Figure 7.
Figure 7: An example of indoor semantic navigation queries in IndoorDB.

4. Future Research Topics

4.1. Privacy Protection in Indoor Space

With the development of indoor positioning systems and indoor location-based services (LBSs), privacy risks have been a threat to the user of indoor mobile information systems, especially the enthusiasts of LBSs. According to [75], two sources of information have a great privacy risk. One is location privacy, because the large amount of location traces generated by indoor positioning devices may be exposed to the untrusted LBS provider; thus, it leads to location privacy threats to the user. The other one is the content privacy. The untrusted service provider may find users’ interests or interpersonal relationship by understanding the information requested by the mobile clients. The leakage of either location information or personal information may cause serious problems. So it is necessary for the research on privacy protection in indoor space.

A typical method of location privacy protection is spatial-cloaking-based technique [76]. The principle of this method is to add uncertainty to the location information which is exposed to the location service, and the spatial-cloaked region is constructed to ensure that there are several users who are located in the same region.

There was a concern about location privacy protection for several years in the outdoor space, so there have been already a large number of solutions for location information preserving. However, content privacy protection is a new subject growing up with the development of LBSs. So maybe it will be an issue worth of research in the future.

4.2. Indoor Trajectory Analysis

So far, there are few researches on indoor history trajectory analysis. Mining the history trajectory data can help managers or administrators have a better understanding about the indoor space they manage. For instance, moving objects density analysis can identify the hotspots in indoor space [77]; the fire escape near these hotspots should be kept clear in case of emergency occurs. A frequent movement pattern analysis can find customers’ trend and interest, and this analysis can be used for personalized recommendation.

A simple method to identify the hotspots can work like this: given a time period , a velocity threshold , and a density threshold , a shop can be regarded as a hotspot when it has a higher costumer density than whereas the costumers have a lower moving speed than during time .

Also with hotspots detection, indoor density query is another way of analysis of indoor trajectory. Finding the dense locations in large indoor spaces is very useful for getting overloaded locations, security, crowd management, indoor navigation, and guidance. Recently, there have been some researches on this topic such as [78, 79].

4.3. Integration of Indoor and Outdoor Data Management

The techniques of spatial-temporal data management in outdoor space have been already very mature at present. There are many available systems for positioning, navigation, and so on. On the other hand, indoor data management is just in the primary stage; many issues are waiting for solution. However, it is predictable that indoor and outdoor systems will undergo integration in the future.

One of the most challenging tasks is to switch seamlessly between indoor and outdoor environment. The location return from GPS positioning system is in a form of latitude and longitude coordinates, called absolute coordinates. Database should record the absolute coordinate just before entering indoor space and keep modifying the recorded coordinate when an object moves in indoor space, so that when object switches indoor space into outdoor space, the latitude and longitude coordinates will be the proper value.

5. Conclusions

Indoor moving objects data management has been a hot research topic due to the rapid development of indoor positioning technologies and location-based services. In this paper, we first analyze the special features of indoor spaces and indoor location-based services. Then, we summarize the main research issues of indoor mobile information systems. These issues include indoor positioning technologies, modeling of indoor spaces, indoor-space-based queries, and indexes for indoor spaces and moving objects. For each research issue, we discuss the significance of the issue and further give a detailed description on the research problems involved in the issue. In addition, we present the recent works and advances on each direction.

After a review on key issues and existing work on indoor mobile information systems, we briefly introduce IndoorDB, which is an indoor mobile information system that was proposed and implemented by our previous studies. IndoorDB is an integration of several indoor-based technologies such as indoor-space models, indoor data storage, indoor query processing, and indoor map interfaces. After describing the data model of IndoorDB, we explain the implementation techniques of IndoorDB, including its architecture on an object-relational DBMS, the data type system, and the user interface to demonstrate its support to indoor queries.

Further, we propose a few future research directions for indoor mobile information systems, including privacy protection in indoor spaces, indoor trajectory analysis, and integration of indoor and outdoor spaces. There are also some other interesting topics related to indoor mobile information system but we believe that these are of the most importance in future studies.

In summary, this paper offers a systematic review on indoor mobile information systems, which is helpful to researchers in indoor moving object databases and other related areas. To the best of our knowledge, the proposed IndoorDB system is the first prototype in indoor-space-related information systems. Presently, IndoorDB has some limitations on performance and functionality. In the future, we will concentrate on optimizing IndoorDB. For example, we will consider automatically importing indoor maps with CAD formats [80] and integrating indoor-space-based indexes into IndoorDB to improve query performance.

Competing Interests

The authors declare that there are no competing interests regarding the publication of this paper.

Acknowledgments

This work is partially supported by the National Science Foundation of China (61379037 and 61472376) and the Fundamental Research Funds for the Central Universities.

References

  1. E. Karbab, D. Djenouri, S. Boulkaboul, and A. Bagula, “Car park management with networked wireless sensors and active RFID,” in Proceedings of the IEEE International Conference on Electro/Information Technology (EIT '15), pp. 373–378, IEEE, Dekalb, Ga, USA, May 2015. View at Publisher · View at Google Scholar
  2. Y.-C. Chen and J.-C. Juang, “Outlier-detection-based indoor localization system for wireless sensor networks,” International Journal of Navigation and Observation, vol. 2012, Article ID 961785, 11 pages, 2012. View at Publisher · View at Google Scholar · View at Scopus
  3. X. Li, J. Wang, and L. Yang, “Outlier detection for indoor vision-based navigation applications,” in Proceedings of the 24th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS '11), pp. 3617–3627, September 2011. View at Scopus
  4. M. Werner, Indoor Location-Based Services, Springer, 2014. View at Publisher · View at Google Scholar
  5. C. Di Flora, M. Ficco, S. Russo, and V. Vecchio, “Indoor and outdoor location based services for portable wireless devices,” in Proceedings of the 25th IEEE International Conference on Distributed Computing Systems Workshops, pp. 244–250, IEEE, 2005. View at Publisher · View at Google Scholar · View at Scopus
  6. H. Surmann, J. Huser, and L. Peters, “A fuzzy system for indoor mobile robot navigation,” in Proceedings of the IEEE International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and The Second International Fuzzy Engineering Symposium, pp. 71–76, Yokohama, Japan, March 1995. View at Publisher · View at Google Scholar
  7. L. Ran, S. Helal, and S. Moore, “Drishti: an integrated indoor/outdoor blind navigation system and service,” in Proceedings of the 2nd IEEE Annual Conference on Pervasive Computing and Communications (PerCom '04), pp. 23–30, IEEE, Orlando, Fla, USA, March 2004. View at Publisher · View at Google Scholar
  8. A. Miu, Design and implementation of an indoor mobile navigation system [Ph.D. Dissertation], Massachusetts Institute of Technology, Cambridge, Mass, USA, 2002.
  9. C. S. Jensen, H. Lu, and B. Yang, “Indoor—a new data management frontier,” IEEE Data Engineering Bulletin, vol. 33, no. 2, pp. 12–17, 2010. View at Google Scholar
  10. A. Baniukevic, D. Sabonis, C. S. Jensen, and H. Lu, “Improving Wi-Fi based indoor positioning using bluetooth add-ons,” in Proceedings of the 12th IEEE International Conference on Mobile Data Management (MDM '11), pp. 246–255, Lulea, Sweden, June 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. H. Lu, X. Cao, and C. S. Jensen, “A foundation for efficient indoor distance-aware query processing,” in Proceedings of the IEEE 28th International Conference on Data Engineering (ICDE '12), pp. 438–449, Washington, DC, USA, April 2012. View at Publisher · View at Google Scholar · View at Scopus
  12. B. Yang, H. Lu, and C. S. Jensen, “Probabilistic threshold k nearest neighbor queries over moving objects in symbolic indoor space,” in Proceedings of the 13th International Conference on Extending Database Technology: Advances in Database Technology (EDBT '10), pp. 335–346, ACM, March 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Alamri, D. Taniar, M. Safar, and H. Al-Khalidi, “Spatiotemporal indexing for moving objects in an indoor cellular space,” Neurocomputing, vol. 122, pp. 70–78, 2013. View at Publisher · View at Google Scholar · View at Scopus
  14. M. Worboys, “Modeling indoor space,” in Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness (ISA '11), pp. 1–6, ACM, Chicago, Ill, USA, November 2011. View at Publisher · View at Google Scholar
  15. L. A. Walton and M. Worboys, “A qualitative bigraph model for indoor space,” in Geographic Information Science: 7th International Conference, GIScience 2012, Columbus, OH, USA, September 18–21, 2012. Proceedings, vol. 7478 of Lecture Notes in Computer Science, pp. 226–240, Springer, Berlin, Germany, 2012. View at Publisher · View at Google Scholar
  16. L. Yang and M. Worboys, “Similarities and differences between outdoor and indoor space from the perspective of navigation,” in Proceedings of the International Conference on Spatial Information Theory (COSIT '11), Belfast, Me, USA, 2011.
  17. L. Yang and M. Worboys, “A navigation ontology for outdoor-indoor space,” in Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Indoor Spatial Awareness (ISA '11), pp. 31–34, Chicago, Ill, USA, November 2011. View at Publisher · View at Google Scholar
  18. H. Liu, H. Darabi, P. Banerjee, and J. Liu, “Survey of wireless indoor positioning techniques and systems,” IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, vol. 37, no. 6, pp. 1067–1080, 2007. View at Publisher · View at Google Scholar · View at Scopus
  19. Y. Gu, A. Lo, and I. Niemegeers, “A survey of indoor positioning systems for wireless personal networks,” IEEE Communications Surveys & Tutorials, vol. 11, no. 1, pp. 13–32, 2009. View at Publisher · View at Google Scholar · View at Scopus
  20. H. Koyuncu and S. H. Yang, “A survey of indoor positioning and object locating systems,” International Journal of Computer Science and Network Security, vol. 10, no. 5, pp. 121–128, 2010. View at Google Scholar
  21. R. Mautz, Indoor positioning technologies [Ph.D. Dissertation], ETH Zürich, Zürich, Switzerland, 2012.
  22. S. Lee, Y. Chon, and H. Cha, “Smartphone-based indoor pedestrian tracking using geo-magnetic observations,” Mobile Information Systems, vol. 9, no. 2, pp. 123–137, 2013. View at Publisher · View at Google Scholar · View at Scopus
  23. M. Muñoz-Organero, P. J. Muñoz-Merino, and C. Delgado Kloos, “Using bluetooth to implement a pervasive indoor positioning system with minimal requirements at the application level,” Mobile Information Systems, vol. 8, no. 1, pp. 73–82, 2012. View at Publisher · View at Google Scholar · View at Scopus
  24. X. Xie, H. Lu, and T. B. Pedersen, “Efficient distance-aware query evaluation on indoor moving objects,” in Proceedings of the 29th International Conference on Data Engineering (ICDE '13), pp. 434–445, Brisbane, Australia, April 2013. View at Publisher · View at Google Scholar · View at Scopus
  25. S. Alamri, D. Taniar, and M. Safar, “Indexing moving objects in indoor cellular space,” in Proceedings of the 15th International Conference on Network-Based Information Systems (NBIS '12), pp. 38–44, IEEE, Victoria, Australia, September 2012. View at Publisher · View at Google Scholar · View at Scopus
  26. C. S. Jensen, H. Lu, and B. Yang, “Indexing the trajectories of moving objects in symbolic indoor space,” in Advances in Spatial and Temporal Databases, N. Mamoulis, T. Seidl, T. B. Pedersen, K. Torp, and I. Assent, Eds., vol. 5644 of Lecture Notes in Computer Science, pp. 208–227, 2009. View at Publisher · View at Google Scholar
  27. Z. B. Gan, Y. G. Yuan, and Y. Z. Zhao, “Indoor moving objects index research based on DR-tree,” Computer Science, vol. 39, no. 10, pp. 177–181, 2012. View at Google Scholar
  28. S. S. Shin, G. Kim, and H. Bae, “Adaptive cell-based index for moving objects in indoor,” KSII Transactions on Internet and Information Systems, vol. 6, no. 7, pp. 1815–1830, 2012. View at Publisher · View at Google Scholar · View at Scopus
  29. P. Bolliger, “Redpin—adaptive, zero-configuration indoor localization through user collaboration,” in Proceedings of the 1st ACM International Workshop on Mobile Entity Localization and Tracking in GPS-Less Environments (MELT '08), pp. 55–60, ACM, San Francisco, Calif, USA, September 2008. View at Publisher · View at Google Scholar
  30. R. Hansen, R. Wind, C. S. Jensen, and B. Thomsen, “Algorithmic strategies for adapting to environmental changes in 802.11 location fingerprinting,” in Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN '10), pp. 1–10, Zurich, Switzerland, September 2010. View at Publisher · View at Google Scholar · View at Scopus
  31. Y.-C. Chen, J.-R. Chiang, H.-H. Chu, P. Huang, and A. W. Tsui, “Sensor-assisted wi-fi indoor location system for adapting to environmental dynamics,” in Proceedings of the 8th ACM International Symposium on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM '05), pp. 118–125, 2005. View at Publisher · View at Google Scholar
  32. T. Gallagher, B. Li, A. Kealy, and A. G. Dempester, “Trials of commercial Wi-Fi positioning systems for indoor and urban canyons,” in Proceedings of the International Symposium on GPS/GNSS, Jeju, Republic of Korea, November 2009.
  33. T. J. Gallagher, B. Li, A. G. Dempster, and C. Rizos, “A sector-based campus-wide indoor positioning system,” in Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN '10), pp. 1–8, IEEE, Zurich, Switzerland, September 2010. View at Publisher · View at Google Scholar · View at Scopus
  34. Z. Chen, H. Zou, H. Jiang, Q. Zhu, Y. C. Soh, and L. Xie, “Fusion of WiFi, smartphone sensors and landmarks using the kalman filter for indoor localization,” Sensors, vol. 15, no. 1, pp. 715–732, 2015. View at Publisher · View at Google Scholar · View at Scopus
  35. G. Caso and L. De Nardis, “On the applicability of Multi-Wall Multi-Floor propagation models to WiFi Fingerprinting Indoor Positioning,” in Future Access Enablers for Ubiquitous and Intelligent Infrastructures, vol. 159 of Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, pp. 166–172, Springer, Berlin, Germany, 2015. View at Publisher · View at Google Scholar
  36. M. Baum, B. Niemann, F. Abelbeck, D.-H. Fricke, and L. Overmeyer, “Qualification tests of HF RFID foil transponders for a vehicle guidance system,” in Proceedings of the 10th International IEEE Conference on Intelligent Transportation Systems (ITSC '07), pp. 950–955, IEEE, Seattle, Wash, USA, October 2007. View at Publisher · View at Google Scholar · View at Scopus
  37. October 2015, http://www.fricknet.com/.
  38. J. Peng, M. Zhu, and K. Zhang, “New algorithms based on sigma point Kalman filter technique for multi-sensor integrated RFID indoor/outdoor positioning,” in Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN '11), pp. 21–23, Guimaraes, Portugal, September 2011.
  39. F. Seco, C. Plagemann, A. R. Jiménez, and W. Burgard, “Improving RFID-based indoor positioning accuracy using Gaussian processes,” in Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN '10), pp. 1–8, September 2010. View at Publisher · View at Google Scholar · View at Scopus
  40. October 2015, http://www.kimaldi.com/.
  41. M. Hasani, J. Talvitie, L. Sydanheimo, E. Lohan, and L. Ukkonen, “Hybrid WLAN-RFID indoor localization solution utilizing textile tag,” IEEE Antennas and Wireless Propagation Letters, vol. 14, pp. 1358–1361, 2015. View at Publisher · View at Google Scholar
  42. K.-J. Li, “Indoor space: a new notion of space,” in Web and Wireless Geographical Information Systems: 8th International Symposium, W2GIS 2008, Shanghai, China, December 11–12, 2008. Proceedings, vol. 5373 of Lecture Notes in Computer Science, pp. 1–3, Springer, Berlin, Germany, 2008. View at Publisher · View at Google Scholar
  43. T. H. Kolbe, G. Gröger, and L. Plümer, “CityGML: interoperable access to 3D city models,” in Geo-Information for Disaster Management, pp. 883–899, Springer, Berlin, Germany, 2005. View at Google Scholar
  44. A. Elfes, “Using occupancy grids for mobile robot perception and navigation,” Computer, vol. 22, no. 6, pp. 46–57, 1989. View at Publisher · View at Google Scholar · View at Scopus
  45. D. Demyen and M. Buro, “Efficient triangulation-based pathfinding,” in Proceedings of the 21st National Conference on Artificial Intelligence (AAAI '06), vol. 6, pp. 942–947, Boston, Mass, USA, July 2006.
  46. M. Mekni, Automated Generation of Geometrically-Precise and Semantically-Informed Virtual Geographic Environments Populated with Spatially-Reasoning Agents, Universal-Publishers, 2010.
  47. N. Wang, P. Jin, Y. Xiong, and L. Yue, “A multi-granularity grid-based graph model for indoor space,” International Journal of Multimedia and Ubiquitous Engineering, vol. 9, no. 4, pp. 157–170, 2014. View at Publisher · View at Google Scholar · View at Scopus
  48. D. Van Zwynsvoorde, T. Siméon, and R. Alami, “Incremental topological modeling using local Voronoi-like graphs,” in Proceedings of the International Conference on Intelligent Robots and Systems (IROS '00), vol. 2, pp. 897–902, Takamatsu, Japan, October-November 2000.
  49. C. Becker and F. Dürr, “On location models for ubiquitous computing,” Personal and Ubiquitous Computing, vol. 9, no. 1, pp. 20–31, 2005. View at Publisher · View at Google Scholar · View at Scopus
  50. T. Ben, X. Qin, and N. Wang, “A semantic-based indexing for indoor moving objects,” International Journal of Distributed Sensor Networks, vol. 2014, Article ID 424736, 12 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  51. D. Li and D. L. Lee, “A topology-based semantic location model for indoor applications,” in Proceedings of the 16th ACM SIGSPATIAL International Conference On Advances In Geographic Information Systems (GIS '08), pp. 439–442, Irvine, Calif, USA, November 2008. View at Publisher · View at Google Scholar · View at Scopus
  52. D. Li and D. L. Lee, “A lattice-based semantic location model for indoor navigation,” in Proceedings of the 9th International Conference on Mobile Data Management (MDM '08), pp. 17–24, IEEE, Beijing, China, April 2008. View at Publisher · View at Google Scholar · View at Scopus
  53. B. Yang, H. Lu, and C. S. Jensen, “Scalable continuous range monitoring of moving objects in symbolic indoor space,” in Proceedings of the ACM 18th International Conference on Information and Knowledge Management (CIKM '09), pp. 671–680, ACM, November 2009. View at Publisher · View at Google Scholar · View at Scopus
  54. C. S. Jensen, H. Lu, and B. Yang, “Graph model based indoor tracking,” in Proceedings of the 10th International Conference on Mobile Data Management: Systems, Services and Middleware (MDM '09), pp. 122–131, Taipei, Taiwan, May 2009. View at Publisher · View at Google Scholar · View at Scopus
  55. H. Lu, B. Yang, and C. S. Jensen, “Spatio-temporal joins on symbolic indoor tracking data,” in Proceedings of the IEEE 27th International Conference on Data Engineering (ICDE '11), pp. 816–827, IEEE, April 2011. View at Publisher · View at Google Scholar · View at Scopus
  56. H. Kim, C. Jun, and H. Yi, “A SDBMS-based 2D-3D hybrid model for indoor routing,” in Proceedings of the 10th International Conference on Mobile Data Management: Systems, Services and Middleware (MDM '09), pp. 726–730, Taipei, Taiwan, May 2009. View at Publisher · View at Google Scholar · View at Scopus
  57. X. Li, C. Claramunt, and C. Ray, “A grid graph-based model for the analysis of 2D indoor spaces,” Computers, Environment and Urban Systems, vol. 34, no. 6, pp. 532–540, 2010. View at Publisher · View at Google Scholar · View at Scopus
  58. A. Jamali, A. A. Rahman, P. Boguslawski, and C. M. Gold, “An automated 3D indoor topological navigation network modeling,” ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 2, no. 2, pp. 47–53, 2015. View at Publisher · View at Google Scholar
  59. C.-Y. Lin, W.-C. Peng, and Y.-C. Tseng, “Efficient in-network moving object tracking in wireless sensor networks,” IEEE Transactions on Mobile Computing, vol. 5, no. 8, pp. 1044–1056, 2006. View at Publisher · View at Google Scholar · View at Scopus
  60. Y. Zhang, C. Jin, and H. Hu, “BFSQ: handling spatial membership query,” Journal of Frontiers of Computer Science and Technology, vol. 4, no. 8, pp. 692–699, 2010. View at Google Scholar
  61. H. Wang, P. Jin, L. Zhao, L. Zhang, and L. Yue, “Generating semantic-based trajectories for indoor moving objects,” in Web-Age Information Management: WAIM 2011 International Workshops: WGIM 2011, XMLDM 2011, SNA 2011, Wuhan, China, September 14–16, 2011, Revised Selected Papers, vol. 7142 of Lecture Notes in Computer Science, pp. 13–25, Springer, Berlin, Germany, 2012. View at Publisher · View at Google Scholar
  62. J. Xu and R. H. Güting, “Manage and query generic moving objects in secondo,” Proceedings of the VLDB Endowment, vol. 5, no. 12, pp. 2002–2005, 2012. View at Publisher · View at Google Scholar
  63. T. Becker, C. Nagel, and T. H. Kolbe, “Supporting contexts for indoor navigation using a multilayered space model,” in Proceedings of the 10th International Conference on Mobile Data Management: Systems, Services and Middleware (MDM '09), pp. 680–685, Taipei, Taiwan, May 2009. View at Publisher · View at Google Scholar · View at Scopus
  64. I. Afyouni, R. Cyril, and C. Christophe, “Spatial models for context-aware indoor navigation systems: a survey,” Journal of Spatial Information Science, vol. 1, no. 4, pp. 85–123, 2012. View at Google Scholar
  65. F. Lyardet, D. W. Szeto, and E. Aitenbichler, “Context-aware indoor navigation,” in Ambient Intelligence, pp. 290–307, Springer, Berlin, Germany, 2008. View at Google Scholar
  66. W. Yuan and M. Schneider, “Supporting continuous range queries in indoor space,” in Proceedings of the 11th IEEE International Conference on Mobile Data Management (MDM '10), pp. 209–214, Kansas City, Mo, USA, May 2010. View at Publisher · View at Google Scholar · View at Scopus
  67. X. Xie, H. Lu, and T. B. Pedersen, “Distance-aware join for indoor moving objects,” IEEE Transactions on Knowledge and Data Engineering, vol. 27, no. 2, pp. 428–442, 2015. View at Publisher · View at Google Scholar · View at Scopus
  68. Y. Wang, G. Yu, Y. Gu, D. Yue, and T. Zhang, “Efficient similarity query in RFID trajectory databases,” in Proceedings of the 11th International Conference on Web-Age Information Management (WAIM '10), pp. 620–631, Jiuzhaigou, China, July 2010.
  69. P. Jin, L. Zhang, L. Zhao, H. Wang, and L. Yue, “Electronic RFID-based indoor moving objects: modeling and applications,” in Advances in Mechanical and Electronic Engineering, vol. 177 of Lecture Notes in Electrical Engineering, pp. 455–461, Springer, Berlin, Germany, 2012. View at Publisher · View at Google Scholar
  70. Y. Theoderidis, M. Vazirgiannis, and T. Sellis, “Spatio-temporal indexing for large multimedia applications,” in Proceedings of the 3rd IEEE International Conference on Multimedia Computing and Systems, pp. 441–448, Hiroshima, Japan, June 1996. View at Publisher · View at Google Scholar
  71. M. A. Nascimento and J. R. O. Silva, “Towards historical R-trees,” in Proceedings of the ACM Symposium on Applied Computing (SAC '98), pp. 235–240, ACM, Atlanta, Ga, USA, February-March 1998. View at Publisher · View at Google Scholar
  72. S. Saltenis, C. S. Jensen, S. T. Leutenegger, and M. A. López, “Indexing the positions of continuously moving objects,” in Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD '00), pp. 331–342, Dallas, Tex, USA, May 2000.
  73. S. Alamri, D. Taniar, M. Safar, and H. Al-Khalidi, “A connectivity index for moving objects in an indoor cellular space,” Personal and Ubiquitous Computing, vol. 18, no. 2, pp. 287–301, 2014. View at Publisher · View at Google Scholar · View at Scopus
  74. P. Jin, L. Zhang, J. Zhao, L. Zhao, and L. Yue, “Semantics and modeling of indoor moving objects,” International Journal of Multimedia and Ubiquitous Engineering, vol. 7, no. 2, pp. 153–158, 2012. View at Google Scholar · View at Scopus
  75. H. Karimi, Indoor Wayfinding and Navigation, CRC Press, New York, NY, USA, 2015. View at Publisher · View at Google Scholar
  76. B. Bamba, L. Liu, P. Pesti, and T. Wang, “Supporting anonymous location queries in mobile environments with privacygrid,” in Proceedings of the 17th International Conference on World Wide Web (WWW '08), pp. 237–246, ACM, April 2008. View at Publisher · View at Google Scholar · View at Scopus
  77. P. Jin, J. Du, C. Huang, S. Wan, and L. Yue, “Detecting hotspots from trajectory data in indoor space,” in Database Systems for Advanced Applications: 20th International Conference, DASFAA 2015, Hanoi, Vietnam, April 20–23, 2015, Proceedings, Part I, vol. 9049 of Lecture Notes in Computer Science, pp. 209–225, Springer, Berlin, Germany, 2015. View at Publisher · View at Google Scholar
  78. T. Ahmed, T. B. Pedersen, and H. Lu, “Finding dense locations in indoor tracking data,” in Proceedings of the 15th IEEE International Conference on Mobile Data Management (MDM '14), vol. 1, pp. 189–194, Brisbane, Australia, July 2014. View at Publisher · View at Google Scholar · View at Scopus
  79. M. Li, Y. Gu, and G. Yu, “Effective and efficient predictive density queries for indoor moving objects,” in Database Systems for Advanced Applications: 20th International Conference, DASFAA 2015, Hanoi, Vietnam, April 20–23, 2015, Proceedings, Part I, vol. 9049 of Lecture Notes in Computer Science, pp. 244–259, Springer, Berlin, Germany, 2015. View at Publisher · View at Google Scholar
  80. D. Xu, P. Jin, X. Zhang, J. Du, and L. Yue, “Extracting indoor spatial objects from CAD models: a database approach,” in Database Systems for Advanced Applications: DASFAA 2015 International Workshops, SeCoP, BDMS, and Posters, Hanoi, Vietnam, April 20–23, 2015, Revised Selected Papers, vol. 9052 of Lecture Notes in Computer Science, pp. 273–279, Springer, Berlin, Germany, 2015. View at Publisher · View at Google Scholar