Mobility data analysis to understand unknown diseases behavior
Transcript of Mobility data analysis to understand unknown diseases behavior
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Mobility data analysis to understand unknown diseases behavior
The case of facial paralysis
Jalel Akaichi
PhD, University of Sciences and Technologies of Lille
Associate Professor, University of Tunis
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General introduction
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Introduction (1/2)
• Our everyday actions, as expressed by the way we live and move, leave digital traces in information systems. – Move in workplace, perform a surgery, etc.
• This is due to the use of mobile location aware devices. – That allow us to communicate. – That allow them to locate us ! Thanks to positioning
technologies.
• Through these traces we can sense the objects movements in a space. – City, delegation, hospitals, human body, etc.
• Their potential value is high because of the increasing volume, pervasiveness and positioning accuracy of these traces. – Varied queries can be performed.
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Introduction (2/2)
• Location technologies are capable of: – Providing a better estimation of a mobile object‘s
position.
• Positioning system-equipped mobile devices can: – Transmit location information to some services
provider.
• Latest advances like : – Wi-Fi and Bluetooth devices are becoming a source of
data for indoor positioning. – Wi-Max can become an alternative for outdoor
positioning.
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Some kinds of mobility data scenarios
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Scenarios in health care (1/4)
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Scenarios in health care (2/4)
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Scenarios in health care (3/4)
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Scenarios in health care (4/4)
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Pervasive systems
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Pervasive computing
• Ubiquitous – Accessible from anywhere.
• Mobile – Integrate mobile devices.
• Context-aware – Take into account the execution context.
• Pervasive – Associate ubiquity, mobility and context-awareness.
• Ambient – Integrated into daily objects.
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Ambient systems
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Pervasive system
Distributed system
Mobility management
Ubiquity management Mobile system
Pervasive system
Context management
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Four waves - Four paradigms
• Mainframe computing (60’s-70’s) – Massive computers to execute big data processing applications. – Very few computers in the world.
• Desktop computing (80’s-90’s) – One computer at every desk to help in business-related activities. – Computers connected in intranets to a massive global network
(internet), all wired.
• Mobile computing (90’s-00’s) – A few devices for every person, small enough to carry around. – Devices connected to cellular networks or WLANs.
• Ubiquitous computing (now) – Tens/hundreds of computing devices in every room/person, becoming
“invisible” and part of the environment. – WANs, LANs, PANs – networking in small spaces.
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Trend: Weiser’s 3 waves of computing
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Ubiquitous computing
• Ubiquitous computing: – Activates the world.
– Is invisible, everywhere computing that does not live on a personal device of any sort, but everywhere.
– Makes a computer so embedded, so fitting, so natural, that we use it without even thinking about it.
• Also called: pervasive, deeply embedded, sentient computing, and ambient intelligence.
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Five main properties for ubiquitous computing
Intelligent
Context-aware
Autonomous
Distributed
iHCI
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Ubiquitous health care
• Environment that collects the information by attaching sensors to medical “objects” and managing real-time information through the network.
• Providing health service of precaution, diagnosis, treatment, post management, etc.
• Everywhere at anytime.
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Blood Sugar
Blood Pressure
Temperature Acceleration
ECG
Ubiquitous health care example
Health Information
System
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Digital bandage
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Low power and small Size
Toumaz company in England Digital Plaster
IMEC in Belgium Wireless Sensor Platform
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MIT Wireless Ring Sensor
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Measurments (wireless)
- Heart rate - Heart rate variability - Oxygen saturation - Estimation of blood pressure
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Enabling technologies
• Wireless (data) communication – Higher bandwidth – Lower power – Commodity (readily
available and secure)
• Small form factor devices – Shrinking electronics – Better displays – New input methods
• Personalization – Machine learning – Inference
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Extremely varied
• Embedding for smart control – Embedded systems for cars, airplanes, patients,
etc.
• Creating new computing devices
• Connecting the existing physical world to a computational infrastructure – Ordinary objects and tasks re-evaluated and
extended with computational/communication capabilities
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Mobile computing
• The application of small, portable, and wireless computing and communication devices.
• Being able to use a computing device even when being on the move.
• Portability is one aspect of mobile computing
– portable vs. mobile.
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Distributed IS duties
• Data persistency. • Data exchange between heterogonous applications. • Data distribution on distant sites. • Data permanent consistency management. • Platforms interoperability. • Applications portability. • Concurrent access management. • Legacy systems integration. • Openness. • Security.
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Pervasive IS duties
• Distributed IS duties.
• Scalability.
• Invisibility.
• Context-awareness.
• Intelligence (« smartness »).
• Pro-action « all the time everywhere ».
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Scalability
• Scaling. • Manage increasing volumes of
– Users. – Applications. – Connected devices.
• Develop applications whose their “heart” is independent from the volume, the users, and the devices.
• Use adaptation techniques to be able to give answers for each case.
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Invisibility
• Requires minimal human intervention.
• Self adaptation to changes.
• Self-learning.
• Example: – Dynamic reconfiguration of network
characteristics of a device.
– Space resources access according to geographic zone encapsulating that space.
– Out of space= limits definition of space.
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Context-awareness
• Perception of the environment to interact more 'naturally' with the user.
• Sensors of the physical environment.
• Self-descriptive devices.
• Persons description.
• Applications meta-data.
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« Smartness »
• Smart = intelligent, quick-witted, malignant, resourceful.
• Perceive the execution context is not sufficient.
• Must effectively use context information.
• Example : smart home.
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Pro-action
• Suggest, propose corrective actions to the user depending on the context present or predicted.
– For example, move 100 meters to a more efficient network and thus accomplish a task in a correct time.
• Implies to know:
– Predict an event, a situation, etc.
– Assess a current or a possible situation.
– Compare two situations and judge the best.
– That "it's worth" to break invisibility.
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Pervasive computing
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Facial paralysis use case
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Motivation scenario: Medical case
• Let us consider a health care organization which is interested: – In analyzing mobility data in different areas such as in facial nerve.
– To decide upon bell’s patients recovery and about the disease behavior.
• It is interested in analyzing: – The recovery process of a patient in time intervals.
– The demographical profiles of patients coming from different geographical zones, belonging to different age intervals, having different family antecedents, etc.
• This knowledge will enable physicians: – To understand the disease behavior.
– To apply more effective strategies according to patients recovery.
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Facial nerve components modeling
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Modeling the facial nerve stream as a moving object circulating into the facial nerve “network”.
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Anatomy of facial nerve
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Facial nerve graph
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Muscles graph Glands Graph
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Mobility data analysis
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Analyzing mobility data (1/3)
• Modern communication and computing devices are pervasive and carried by various objects: people, vehicles, etc.
• The consequence is that object and its activity in a space may be sensed: – Not necessarily on purpose.
• We just collect data.
– As a side effect of the services provided to mobile users. • Calls made and/or received, SMS, emails, shopping, diagnosis, etc.
• Wireless mobile devices network is an infrastructure able: – To gather mobility data..
– To analyze them and gain insights about objects movements. • Trajectories (stop, moves, activities, etc.).
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Analyzing mobility data (2/3)
• Usage of location aware devices allows access to large spatiotemporal datasets.
• The space-time nature of this kind of data: – Results in the generation of huge amounts of
spatiotemporal data.
– Imposes new challenges regarding the analytical tools to be used for transforming raw data to knowledge.
• Necessity to investigate the extension of traditional analytical techniques to be applicable on mobility data.
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Analyzing mobility data (3/3)
• The analysis of mobility data raises opportunities for discovering behavioral patterns to be exploited in applications like: – Mobile marketing. – Mobile information collections. – Mobile hospitals. – Mobile physicians. – Stream nerve detection. – Heart disease supervision. – Robots. – Traffic management, etc.
• OLAP and DM techniques can be employed in order to convert this
vast amount of raw data into useful knowledge.
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Expectations from mobility data analysis (1/3)
• To propose innovative analytical techniques aiming to extract useful patterns from spatiotemporal data. – Extraction, Transformation, and Loading.
– Mobile devices, MOD, TDW.
• To identify the difference between two types of spatiotemporal data: mobility and immobility data. – Stream nerve vs muscles.
• To focus on data warehousing and mining techniques that can be applied on MODs. – Patients, physicians, devices, medical staffs, hospitals,
cities, etc.
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Expectations from mobility data analysis (2/3)
• How traditional data cube model is adapted to TDWs in order to transform raw location data into valuable information.
• How ETL procedure feeds a TDW with aggregated trajectory data.
• How to aggregate cube measures for OLAP purposes.
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Expectations from mobility data analysis (3/3)
• To study a new approach in designing trajectory data cubes.
• To give answers to ad hoc OLAP queries related to various applications.
• To propose a new OLAP data model that include a flexible fact table that: – Can answer queries considering semantic definitions
of trajectories.
– Provides the option to choose the appropriate semantic for aggregation queries over trajectory data.
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Moving object data management, warehousing and mining
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Health care decisional process
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Measured Data
Sensing Monitoring Feedback Analyzing
ETL Long-term Analysis Results
Bioinformatics Feedback / Value-Added Service
Measuring biological signals
Filtered & Analyzed Data Display
Long-term Data storage
Trend analysis
Behavior modification Emergency Alert Feedback-Action (Prescription, exercise, etc)
Trajectory Data Warehousing Solution
Medical Devices OLAP, Data ming
u-Health visualization
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Facial nerve TDW
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Concepts on spatiotemporal data
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Basic concepts on spatiotemporal data (1/3)
• Generation of dissimilar, dynamic, and geographically distributed spatiotemporal data has exploded, thanks to advances in:
– Mobile devices and remote sensors.
– Networks.
– Location sensing devices.
• 2 types of spatiotemporal data: mobile and static.
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Basic concepts on spatiotemporal data (2/3)
• The rate at which geospatial data are generated exceeds the ability to organize and analyze them to extract patterns in a timely manner.
• CS and Geo-informatics collaborate to provide innovative and effective solutions.
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Basic concepts on spatiotemporal data (3/3)
• A typical category of mobility data is the time-stamped
location data: – Collected by location-aware devices. – Allowing access to large datasets consisting of time-stamped
geographical locations.
• The traditional database technology has been extended into MODs that handle: – Modeling. – Indexing. – Query processing issues for trajectories.
• The challenge after storing the data is: – The implementation of analytics to extract useful knowledge.
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Immobile entities (1/2)
• Sensing technologies feel, record and study phenomena. – Human body, diseases, etc.
• At least data collection occurs every one small time unit depending on applications.
• Patients data collection is huge and rapidly increasing.
• Medical staffs record information to describe and study patients bodies activities.
• Analysts find a valuable “data treasure”, to process and analyze to discover knowledge from this data.
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Immobile entities (2/2)
• Human body phenomena are instantly recorded by a number of organizations worldwide.
• A system collecting and analyzing most accurate data among different sources is needed.
• Some sources provide data about the same phenomena though with differences in their details.
• The need for a generic architecture will be able to integrate the remote sources in a proper way by refining and homogenizing raw data.
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Data warehousing and mining components
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Data warehousing and mining components
• Tools for data exploration and inspection.
• Algorithms for generating historic profiles of activities related to specific spaces and time periods.
• Techniques providing the association of data with other geophysical parameters of interest: – Patient morphology, disease and recovery evolution, etc.
– Visualization components using geographic and other thematic-oriented maps for: • Presentation of data to users such as medical staffs.
• Supporting sophisticated user interaction.
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Trajectory data warehousing and mining users profiles
• Physicians are interested in:
– Constructing and visualizing patients' profiles of certain body regions during specific time periods of a disease evolution.
– Discovering regions of similar behavior.
– Disease activity, thus querying the system for properties of general interest.
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Facial paralysis warehousing and mining
• DWMS architecture provides users a wealth of information about patients recovery from Bell’s palsy recovery.
• Collected data can be stored in a local database and/or a data warehouse (for simple querying and analysis for decision making, respectively).
• Data within the database is dynamic and detailed; while that within the data warehouse is static and summarized.
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DWMS querying functionality
• Retrieval of spatial information given a temporal instance.
– When we are dealing with records including: • Position (segments).
• Time of facial nerve partial or total recovery together with attributes like intensity, segments, paths, muscles, etc.
• Retrieval of spatial information given a temporal interval.
– Evolution of spatial objects (stream nerve) over time.
– Recording the duration of partial and total recoveries and how certain parameters of the phenomenon vary throughout the time interval of its duration.
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Examples of typical queries
• Find the number of recoveries realized during the past four months, which reside more closely to a given location.
• Find all sequelae of patients residing in a certain region.
• Find the number of recoveries occurred in a specified time interval.
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Maintaining summary for data analysis (1/3)
• Two popular techniques for analyzing data and interpreting their meaning are: – OLAP analysis. – Data mining.
• Summarized health care data can study the phenomenon from a higher level and search for hidden, previously unknown knowledge.
• View part of the historical recovery profile: – Example. the number of cases that leads to a surgery in the past
twenty years, over a specified region.
• View the same information over a country, continent, the world. – More detailed view, formally a drill-down operation. – Worldwide. More summarized view, formally a roll-up operation.
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Maintaining summary for data analysis (2/3)
• Slice and dice, for selecting parts of a data cube by imposing conditions on a single or multiple cube dimensions.
• Pivot, which provides the user with alternative presentations of the cube.
• Integrating data analysis and mining techniques into an DWMS aims to the discovery of interesting, implicit and previously unknown knowledge.
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Examples of useful patterns found through
Knowledge Discovery & Delivery (KDD) process
• Clustering of information. – Cases occurred closely in space and/or time.
– Cases related to kids, adults, etc.
• Classification of phenomena with respect to recovered areas, and detecting phenomena semantics by using pattern finding techniques, etc. – Characterizing the main recovery aspects in recovery
sequences.
– Measuring the similarity of sequelae sequences, according to a similarity measure specified by the domain expert, etc.
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Mobile entities
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The case of trajectory data
• Moving objects are geometries (i.e. points, lines, areas) changing over time. – Pills, stunts, stream nerve, etc.
• Trajectory data describes the movement of these objects.
• Movement implies two dimensions: the spatial and the temporal (recovery).
• Movement can be described as continuous change of position in the geographical space and through comparison between two different temporal instants.
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Spatio-temporal trajectory
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• A sequence of spatiotemporal points
• In a road network.
• In a human body network.
• In a part a body network: The face for example.
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A semantic trajectory example
• Semantic enrichment integrate structured trajectories with semantic knowledge from the two semantic viewpoints, i.e.:
– Geographic view.
– Application domain view.
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A hybrid semantic trajectory model
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Structured trajectory (a sequence of episodes)
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Semantic trajectory (a sequence of semantic episodes)
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Offline trajectory computing framework
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Formalization
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Formalization (1/3)
• A trajectory T is a continuous mapping from the temporal I ⊆ R to the spatial domain (R2, the 2D plane).
• I R R2: t a(t)= (ax(t), ay(t)).
• T= {(ax(t), ay(t), t) | t I} R2xR.
• Where (ax(t), ay(t), t) are the sample points contained in the available dataset.
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Formalization (2/3)
• From an application point of view: – A trajectory is the recording of an object‘s motion.
– Example. The recording of the positions of an object at specific timestamps.
• The actual trajectory consists of a curve.
• Real-world requirements imply that the trajectory has to be built upon a set of sample points. – The time-stamped positions of the object.
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Formalization (3/3)
• Trajectories of moving points are often defined as sequences of (x, y, t) triples.
• T={(x1, y1, t1), (x2, y2, t2), …, (xn, yn, tn)}, where xi, yi, ti R, and t1< t2 … <tn.
• The main objective is to include appropriate techniques for the representation, querying, indexing and modeling of moving object‘s trajectories.
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Innovation
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The need for innovation in decision support techniques (1/2)
• Traditional decision support techniques developed as a set of applications and technologies for: – Gathering.
– Storing.
– Analyzing.
– Providing access to data.
• Example: – Data warehousing.
– Online analytical processing.
– Data mining.
– Visualization.
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The need for innovation in decision support techniques (2/2)
• These techniques are embedded in decision support systems. – To support business and organizational decision-making
activities.
• Such systems help decision makers to identify, analyze and solve problems as well as make decisions, by combining: – Raw data. – Documents. – Personal knowledge. – Business models, etc.
• Decision support techniques were developed to satisfy the changeable and complicated needs of current business and technological environment.
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Decision support techniques extensions (1/2)
• The extension of traditional techniques to deliver new analytics, suitable for mobility data.
• To serve emerging applications (e.g. mobile health care) that need to convert raw location data into useful knowledge.
• A TDW can help towards computing aggregations on trajectory data and thus studying them in a higher level of abstraction.
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Decision support techniques extensions (2/2)
• Data mining techniques are used to discover unknown, useful patterns.
• The vast amount of available mobility data requires the extension of traditional mining techniques so as to be suitable for this new kind of data.
• Discovering spatiotemporal associations, clusters, predicting actions, etc., lead to mobility patterns: – That could help to construct summary and useful abstractions of large
volumes of raw location data and gain insights on movement behaviors.
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Efficient trajectory data warehousing
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Efficient trajectory data warehousing (1/2)
• Data warehousing is a technology for integrating all sorts of transactional data, dispersed within organizations.
• A DW is defined as a subject-oriented, integrated, time-variant, non-volatile collection of data in support of management of decision making process.
• In a DW, data are organized and manipulated in accordance with the concepts and operators provided by a multidimensional data model, which views data in the form of a data cube.
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Efficient trajectory data warehousing (2/2)
• A data cube allows data to be modeled and viewed in multiple dimensions: – Each dimension represents some business perspective.
– It is typically implemented by adopting a star, snowflake, or constellations schema model.
• A DW consists of a fact table surrounded by a set of dimensional tables related with it, which contains keys and measures.
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Dimensions and measures
• Dimensions represent the analysis axes, while measures are the variables being analyzed over the different dimensions.
• Each dimension is organized as a hierarchies of dimension levels; each level corresponding to a different granularity for the dimension.
• The members of a certain dimension level (months) can be aggregated to constitute the members of the next higher level (years).
• The measures are also aggregated following this hierarchy by means of an aggregation function.
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OLAP Operations (1/2)
• DWs are optimized for OLAP operations.
• Typical OLAP operations include:
– The aggregation or de-aggregation of information (called roll-up and drill-down, respectively) along a dimension.
– The selection of specific parts of a cube (slicing and dicing).
– The reorientation of the multidimensional view of the data on the screen (pivoting).
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OLAP Operations (2/2)
• Data warehousing and OLAP techniques can be employed in order to convert vast amount of raw data into useful knowledge.
• Conventional techniques were not designed for analyzing trajectory data.
• There is the need for extending data warehousing technology so as to handle mobility data.
• Such a warehouse could analyze measures: he number of patients in specific spatial areas, the average intensities of facial nerve, the maximum and average speed of a stream nerve.
• This analysis could be done through appropriate dimensions that will allow to explore aggregated data under different granularities.
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Motivation issues (1/2)
• Transform raw trajectories to valuable information used for decision making in ubiquitous applications.
• What makes extracting valuable information from such spatiotemporal data a hard task: – The high volume of raw data produced by sensing and
positioning technologies.
– The complex nature of data stored in trajectory databases.
– The specialized query processing demands.
• Extend traditional aggregation techniques to produce summarized trajectory information and provide OLAP style analysis.
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Motivation Issues (2/2)
• Extending traditional (i.e., non-spatial), spatial or spatiotemporal models to incorporate semantics driven by the nature of trajectories introduce specific requirements: – Support high level OLAP analysis. – Facilitate knowledge discovery from TDWs.
• The basic analysis constituents in a TDW (i.e. facts) are the trajectories themselves.
• We categorize the identified requirements into modeling, analysis and management requirements. – The first considers logical and conceptual level challenges
introduced by TDWs. – The second goes over OLAP analysis requirements. – The third focuses on more technical aspects.
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Data cube modeling
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Data cube modeling issues
• Investigate the prerequisites and the constraints imposed:
–When describing the design of a TDW from a user perspective (i.e. conceptual model).
–When describing the final result as a system in a platform-independent tool (i.e. logical model).
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Thematic, spatial, temporal measures
• From a modeling point of view: – A trajectory is a spatial object whose location varies in time.
• Trajectories have thematic properties: – Usually are space and time dependent.
• Characteristics of trajectories are described to be analyzed: – Numeric characteristics, such as the average speed of the
trajectory, its direction, its duration. – Spatial characteristics, such as the geometric shape of the
trajectory. – Temporal characteristics, such as the timing of the movement. – Spatiotemporal characteristics; such as a representative
trajectory or a cluster of trajectories.
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The portions of trajectories that lie within a cell
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Thematic, spatial, temporal measures
• Depending on the application and user requirements, several numeric measures could be considered.
• The number of trajectories found in the cell (or started/ended their path in the cell; or crossed/entered/left the cell, and so on).
• The {average/min/max} distance covered by trajectories in the cell.
• The {average/min/max} time required to cover this distance.
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Thematic, spatial, temporal dimensions (1/4)
• Regarding the supported dimensions, as starting point a TDW should support: – The classic spatial dimensions (e.g. coordinate,
roadway, district, cell, city, province, country).
– Temporal dimensions (e.g. second, minute, hour, day, month, year).
– Hierarchies, describing the underlying spatiotemporal framework wherein trajectories are moving.
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Thematic, spatial, temporal dimensions (2/4)
• It is important to allow space-time related dimensions to interact with thematic dimensions describing other sorts of information regarding trajectories like: – Technographic (mobile device used).
– Demographic data (age and gender of users).
• This allow an analyst : – To query TDW about the number of objects crossed an area of
interest: get quantitative information.
– To identify the objects in question: get qualitative information.
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Thematic, spatial, temporal dimensions (3/4)
• A rich TDW schema should include the following dimensions: – Temporal (time). – Geographical (location). – Demographics (gender, age, occupation, marital status, home
and work postal code, etc.). – Technographics (mobile device, sensors, subscriptions in special
services, etc.).
• Technographics and demographics dimensions: – Enhance the warehouse with semantic information. – Allow the grouping of trajectories according to demographical
characteristics or based on the technological characteristics of their devices.
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Thematic, spatial, temporal dimensions (4/4)
• Trajectory is a set of sampled locations in time, for which:
– The in-between positions are calculated through some kind of interpolation.
– The lowest level information is that of spatial coordinates.
• This implies a huge discretization of the spatial dimension: cell positions could be used instead of point positions.
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OLAP
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OLAP Requirements
• In traditional DWs, data analysis is performed interactively by applying a set of OLAP operators.
• In spatial data warehousing, particular OLAP operators have been defined to tackle the specificities of the domain.
• We expect algebra of OLAP operators to be defined for trajectory data analysis.
• Such an algebra should include: – Traditional operators, such as roll-up, drill-down and
selection properly tailored to trajectories. – Additional operators which account of the spatiotemporal
data type.
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Roll-up
• Roll-up operation allows to navigate from a detailed to a more general level of abstraction:
– Either by climbing up the concept hierarchy (e.g. from the level of ‘city‘ to the level of ‘country‘).
– Or by some dimension reduction (e.g. by ignoring the ‘time‘ dimension and performing aggregation only over the ‘location‘ dimension).
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Drill-down
• Drill-down operation is the reverse of roll-up.
• It allows to navigate from less detailed to more detailed information by:
– Either stepping down a concept hierarchy for a dimension (‘country‘ to ‘city‘).
– Or by introducing additional dimensions (e.g. by considering not only the ‘location‘ dimension but the ‘time‘ dimension also).
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Slice, Dice
• Slice operation performs a selection over one dimension (‘city=Tunis‘), whereas dice operation involves selections over two or more dimensions (‘city=Swarthmore and year=2006‘).
• The conditions can involve not only numeric values but also more complex criteria, like spatial and/or temporal query windows.
• To support these operations, the selection criteria can be transformed into a query against the TDW and processed by adequate query processing methods.
• OLAP operations should be also supported by a TDW since they provide meaningful information.
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Other operations
• Other operations dedicated to trajectories might be defined. Examples include:
– Operators that dynamically modify the spatiotemporal granularity of measures representing trajectories.
– Medoid etc. operators which apply advanced aggregation methods, such as clustering of trajectories to extract representatives from a set of trajectories.
– Operators to propagate/ aggregate uncertainty and imprecision present in the data of the TDW.
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Trajectory Data Warehousing
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Trajectory Data Warehousing The framework
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Key questions
• How to store and query trajectory data? – Database technology is extended: Moving Object Databases.
• How to reconstruct a trajectory from raw logs? – Position devices provide us information just about location
points and not about trajectories.
• How to analyze trajectory data – Data Warehousing technology is adapted to handle trajectory
data.
• Are there any spatiotemporal patterns in my data? – New data mining algorithms are needed so as to discover such
patterns. – Mobile objects health care mining is a very interesting topic in
this area.
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Data mining (1/2)
• Clustering, the discovery of groups of similar trajectories, together with a summary of each group.
• Which are the main paths of recovery during time intervals.
• Which are the main paths of non recovery during time intervals.
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Data mining (2/2)
• Frequent patterns, the discovery of frequently recovered sub paths.
• Classification, the discovery of behavior rules, aimed at explaining the behavior of current patients.
• Clustering moving object trajectories, for example, requires finding out both: – A proper spatial granularity level (points, segments, lines,
etc.).
– A significant temporal sub domain (e.g., recovery “rush time “ might be informative for defining a clustering structure over recovery data.
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References
• Jalel Akaichi. Mobility data modeling and analysis, Master sc. Course, Jalel Akaichi. • J. Akaichi. Pervasive systems and cloud computing, Master sc. Course. • Frederique laforest. Systèmes d’information pervasifs, Master sc. Course. • Gerasimos D. Marketos. Data Warehousing & Mining Techniques for Moving Object Databases. PhD Thesis. 2009. • Akaichi, J., Oueslati, W. (2011) ‘Facial nerve stream modeling for facial paralysis patients states evolution’, IEEE IRI,
pp. 429-433. • Oueslati, W., Akaichi, J. (2011) ‘A Trajectory UML profile For Modeling Trajectory Data: A Mobile Hospital Use Case’.
International Journal of Advanced Research in Computer Science. • Oueslati, W., Akaichi, J. (2010) ‘Pre-learning from mobile professionals' Trajectory’. International Conference on
Computer Systems and Applications (AICCSA), IEEE/ACS, pp. 1-7. • Oueslati, W., Akaichi, J. (2010) ‘Mobile Information Collectors' Trajectory Data Warehouse Design’, International
Journal of Managing Information Technology (IJMIT) Vol.2, No.3, pp. 1-20. • Oueslati, W., Akaichi, J. (2010) ‘A Survey on Data Warehouse Evolution’, International Journal of Database
Management Systems (IJDMS), Vol.2, No.4, pp. 11-24. • Akaichi, J., Elloumi, L. (2009) ‘Bell’s palsy Disease Recovery Surveillance: Facial Nerve Trajectory Data Modelling’,
Data Integration in the Life Science Workshops (DILS 2009), Manchester. • Khayati, M., Akaichi, J. (2009) ‘Pervasive Assistance System for Mobile Physicians on the Road’, International
Journal of Healthcare Technology Management (IJHTM), Volume 10, No. 1, pp. 82-100. • Akaichi, J. (2009) ‘Mobile Long Life Learner Pervasive Assistance System’, Learning Technology, publication of IEEE
Computer Society’s, Technical Committee on Learning Technology (TCLT), Volume 11, Issue 3.
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Thank you
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