Toward context and preference-aware location

ACM MobiDE'09 - 8th International ACM Workshop on Data Engineering for Wireless and Mobile Access
Toward Context and Preference-Aware Location-based
Mohamed F. Mokbel
Justin J. Levandoski
Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN
The explosive growth of location-detection devices, wireless
communications, and mobile databases has resulted in the
realization of location-based services as commercial products
and research prototypes. Unfortunately, current locationbased applications (e.g., store finders) are rigid as they are
completely isolated from various concepts of user “preferences” and/or “context”. Such rigidness results in nonsuitable services (e.g., a vegetarian user may get a restaurant with non-vegetarian menu). In this paper, we introduce the system architecture of a Context and PreferenceAware Location-based Database Server (CareDB, for short),
currently under development at University of Minnesota,
that delivers personalized services to its customers based
on the surrounding context. CareDB goes beyond the traditional scheme of “one size fits all” of existing location-aware
database systems. Instead, CareDB tailors its functionalities and services based on the preference and context of each
customer. Examples of services provided by CareDB include a restaurant finder application in which CareDB does
not base its choice of restaurants solely on the user location. Instead, CareDB will base its choice on both the user
location and surrounding context (e.g., user dietary restriction, user preferences, and road traffic conditions). Within
the framework of CareDB, we discuss research challenges
and directions towards an efficient and practical realization
of context-aware location-based query processing. Namely,
we discuss the challenges for designing user profiles, multiobjective query processing, context-aware query optimizers,
context-aware query operators, and continuous queries.
Combining the functionally of map software (e.g., Google
maps, Microsoft MapPoint, Yahoo maps), location-detection
devices (e.g., GPS antenna, cellular phones, and RFIDs),
personal handheld devices (e.g., PDA and GPS), wireless
communication, and database systems has resulted in the realization of location-based services as commercial products
(e.g., see [27, 35]) and research prototypes (e.g., see [16,
30, 38]). The main promise of location-based services is
to provide new services to their customers based on the
knowledge of their locations. Examples of these services
include continuous live traffic reports (“Continuously, let
me know if there is congestion within five minutes of my
route”), food and drink finder (“Where is my nearest fast
food restaurant”), and location-based advertising (“Send ecoupons to all cars that are within two miles of my gas station”). According to the Cellular Telecommunication and
Internet Association, CTIA, there are more than 240 Million wireless customers in the Unites States [10] while a recent research report from ABI Research indicated that the
number of location-based services subscribers will be 315
Million by 2011 [1]. Due to the abundance of location-based
data, location-based services have begun to integrate their
functionality with database systems [19, 20, 26, 29]
Figure 1 gives a high level overview of location-based services where users issue various location-based queries (e.g.,
“Where is my nearest restaurant”) through their personal
devices. Mobile devices are connected to a database server
to submit their queries along with their locations that are
retrieved through location-detection devices. Finally, the
database server evaluates the user query based on the user
location, the underlying map, and the locations of objects of
interest (e.g., restaurants). At the user level, the users see
the answer of their queries on the handheld devices guided
by the map layout.
Unfortunately, the current state-of-the-art processing of
location-based queries is rigid, as query processing completely isolates various forms of user “preferences” and/or
“context”. For example, in a restaurant finder application,
the users actually want to find the “best” restaurant according to their current preferences and context. Existing location-based query processors reduce the meaning of
“best” to be only the “closest” restaurant. Any query processing that produces results based on preference and or
context is applied after the location-based database operations. In other words, preference and context are considered afterthought problems in terms of query processing. To
show the rigidness of current techniques, consider the case
Categories and Subject Descriptors
H.2 [Database Management]: Miscellaneous
General Terms
Design, Architecture
This work is supported in part by the National Science
Foundation under Grants IIS 0811998, IIS0811935, and
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ACM MobiDE'09 - 8th International ACM Workshop on Data Engineering for Wireless and Mobile Access
tion, CareDB has the ability to collect other data regarding
the context of database information and surrounding environment. Upon processing a given query, CareDB considers
the following: (1) Existing stored data (this is similar to
traditional databases), (2) Current user profile that includes
user preference and user context (existing systems consider
only the location context while ignoring all other context),
and (3) Surrounding global context (e.g., time, weather, and
road conditions). To show the capabilities of CareDB, consider the aforementioned example of restaurant finder application. Before reporting the query answer, CareDB will
check the user context to get her budget, dietary restriction,
and available time, thus would not report expensive restaurant, restaurants with excessive waiting time, nor restaurants with conflicting dietary offerings. In addition, CareDB
will check the restaurant context in order to know the restaurant current waiting line and its current opening status to be
able to report only opening restaurants that have suitable
waiting time. Finally, CareDB will check the environment
context in terms of current road traffic in order to estimate
the current travel time to each of the reported restaurants.
Within the framework of CareDB, we identify five main
challenges that need to be addressed: (1) matching the user
profile to the current context, (2) supporting multi-objective
query processing as a means of satisfying various forms of
user profiles, (3) designing a context-ware query optimizer
that takes into account surrounding context when deciding
upon the best query plan, (4) designing context-aware query
operators that embed context-awareness into the core processing of traditional location-based query operators, and
(5) supporting continuous queries that are ubiquitous in
context-aware environments.
The rest of this paper is organized as follows: Section 2
covers CareDB system architecture. Section 3 discusses five
main challenges addressed by CareDB with pointers to research directions and solutions. Related is highlighted in
Section 4. Finally, Section 5 concludes the paper.
Figure 1: Location-based services
of a user asking about the nearest five restaurants. After retrieving the answer and going to the first restaurant, the user
discovers that the restaurant has an unbearably long wait,
while the second restaurant ends does not match the user
dietary restrictions. Meanwhile, the third restaurant is too
expensive for the user’s budget, while the fourth restaurant
is currently closed. Finally, the route to the fifth restaurant
is infeasible as there is congestion due to a traffic accident.
The rigidness of such an approach is due to two main reasons: (1) The lack of personalized customer services. For
example, if two persons asking the same query in the same
location, they will get the same answer, even if their personal
preferences are different. (2) The lack of context awareness
as the only considered context is the user location, while
other kinds of context (e.g., weather, personal preference,
and current road conditions) are completely ignored.
In this paper, we aim to raise the challenges and provide
research directions to enable practical realization of preference and context-aware location-based services. The main
idea is to embed various forms of preferences and context
in core processing of location-based queries. To this end,
we are not aiming to define new location-based queries, instead, we aim to redefine the answer of existing locationbased queries. As the query answer may be returned to the
users on their mobile devices with limited screen capabilities, it is of essence to enhance the quality of the answer and
limit the answer to only those tuples that are of major interest to the users according to their preferences and context.
For example, we aim to force traditional restaurant finder
queries to consider user preferences (e.g., dietary restriction, range of price, and acceptable restaurant rating), user
context (e.g., location, available time, and privacy requirements), environmental context (e.g., time, weather, other
user reviews, and current traffic), and database-specific context (e.g., for a restaurant we consider current waiting line,
opening status, rating, and change of menu).
Towards the goal of realizing a context and preferenceaware location-based services, we introduce the system architecture of a preference and context-aware location-based
Database Server (CareDB, for short), currently under development at University of Minnesota, that delivers personalized services to its customers based on the surrounding
context. CareDB will replace the database server depicted
in Figure 1 and will go beyond the traditional scheme of “one
size fits all” of existing location-aware database systems. Instead, CareDB tailors its functionalities and services based
on the context of each customer. System users register their
personal preference profiles/context with CareDB. In addi-
Figure 2 gives the CareDB system architecture that is depicted by a bold rectangle. Other than user queries, there
are three input types for CareDB, namely, the user preference/context, the database-specific context, and the environmental context. Each context has two components, static
context that is rarely changing (represented by solid lines
and dark gray rectangles) and dynamic context that is frequently changing (represented by dotted lines and light gray
rectangles). Details of the input context are as follows:
• User preferences/context. Registered users with
CareDB have the ability to specify their personal preferences along with their context. For example, a user
may specify that whenever she is looking for a restaurant, she would like that the query processor take into
account distance, price, rating, and dietary restriction
while when looking for gas stations, the user wants
to consider only the distance and the preferred gas
company. Using such preferences, the preference- and
context-aware query processor will consult the appropriate context to provide a context-aware answer that
is tailored to both the user preferences and user context. Parts of user context are static, i.e., rarely changing, for example, income, profession, health condition,
ACM MobiDE'09 - 8th International ACM Workshop on Data Engineering for Wireless and Mobile Access
User 1
User 2
User Preferences
and Context
User n
. . . .
. . . .
. . . .
Preference− and
Query Processing
and Optimization
. . . .
. . . .
Data 2
. . . .
Data 1
Data n Data Sources
Figure 2: System Architecture
age, and privacy requirements. Other parts of user
context can be highly dynamic, for example, user location which is continuously changing based on the
user movement.
As depicted in Figure 2, at the front end of the system, all user queries go through a query rewriting module that receives simple snapshot and continuous locationbased queries, investigates what are the context that need
to be considered, and adds the context as conjunctive predicates to the issued query. The core part of CareDB is
the preference- and context-aware query processing and optimization module. The main responsibilities of this module
are to embed context-awareness into the existing core processing of query operators, support the integration between
context-aware queries and a wide class of location-based
queries, and provide support of environmental context as
well as both the user and database context. It is important
to note that the preference- and context-aware query processing module does not support new types of location-based
queries, instead, it enhances the quality of answer of existing location-based queries by incorporating user preferences,
user context, database-specific context, and environmental
context into the query answer.
This section outlines five main challenges and research
directions towards building a context and preference-aware
location-based database server, namely, mapping user profiles into context (Section 3.1), multi-objective queries (Section 3.2), context-aware query optimization (Section 3.3),
context-aware query operators (Section 3.4), and continuous queries (Section 3.5).
• Database-specific context. Data sources can register with CareDB by their context. Examples of data
sources include restaurant databases, hotel databases,
and taxi databases. Similar to the concept of user context, database-specific context may have static and/or
dynamic context types. For example, in a restaurant
database, a static context may include meal price,
dessert price, cuisine, type of people who like this
restaurant, and operating hours while dynamic context may include the current waiting time, the current
number of customers, and today’s special. In order
to find a suitable restaurant for a certain user, the
preference- and context-aware location-based query
processor would match user preferences and context
with the restaurant context.
Challenge I: Preference Profiles
and Contexts
CareDB performs query processing with knowledge of user
preferences and surrounding contexts. Thus, two main challenges exist in managing this information: (a) Define a user
preference profile that reflects user preferences in various dimensions. The more preferences contained in the profile the
more accurate and relevant the query answer will be, yet,
the more complicated query processing will be. (b) Map any
required contextual data to the attributes in the preference
profile. It is of essence to the query optimizer to know exactly the set of context data that must be considered for the
Figure 3 gives an example of a user preference profile when
issuing a restaurant finder query. User profiles are depicted
as bullets while matching user context are depicted as text
inside dotted rectangles. In that profile, the user explicitly specifies that upon looking for a restaurant, she wants
to take into consideration: (a) The restaurant rating that
can be divided into general and special ratings based on
the user dietary preferences. This attribute relies on both
the user context (dietary preference) and the database context (restaurant rating). (b) The restaurant price for main
course and the dessert, that relies on the database context
(restaurant price). (c) The total time needed to start the
meal which can be computed as the travel time from the user
location to the restaurant location plus the waiting time at
the restaurant. This attribute depends on the three context
types, user context (the current user location and available
time), database context (the restaurant location and current
average waiting time), and the environmental context (the
current traffic conditions that can be used to compute the
• Environmental context. Unlike user and database
context that are stored either at the query issuer side
or at the data source side, the environmental context is stored at a third party that is consulted by
the location-based query processor to enhance the answer quality of location-based queries. Examples of
dynamic environmental context include current road
traffic (i.e., estimated travel time through road segments), time, weather, and neighboring peers of the
query issuer. Examples of static environmental context include third-party ratings of restaurants, gas stations, hotels, or any other third-party statistics about
data sources. Examples of queries that get help from
the environmental context include a restaurant finder
query where the query processor consults the environmental context to get the current traffic and estimated
travel time to each restaurant, then, the computed distance is fed to the query to get an accurate answer.
Another example that makes use of the data collected
by third parties is also a restaurant finder query in
which the user wants to choose the best restaurant in
terms of other user reviews and scores to each restaurant.
ACM MobiDE'09 - 8th International ACM Workshop on Data Engineering for Wireless and Mobile Access
considered comparable and can be reduced to only onedimensional value using a scoring function (e.g., sum or average). Such one dimensional value gives a total order of
the query result. Then, the right answer can be simply considered as the answer with the highest score. Furthermore,
the user may specify a number k to return the top k results
as the query answer. On the other hand, in skyline queries,
all dimensions are considered independent as they cannot be
aggregated (e.g., price and distance cannot be aggregated).
In this case, the best effort of the query processor is to eliminate those objects that are dominated by other objects. For
example, if restaurant x is cheaper, closer, and has higher
rating than restaurant y, then there is no need to report
restaurant y in the answer. By eliminating all points that
are dominated by others, the query processor can produce
only skyline points. In the example mentioned above, the
query processor would return r1 and r3 only as r2 is dominated by r1 (r1 is cheaper, closer, and has higher rating
than that of r2 ). Then, it is up to the user to choose the
appropriate answer among the set of skyline points.
Practically, neither top-k nor skyline queries can directly
service context-aware applications as some dimensions are
dependent and can be aggregated (e.g., meal price and
dessert price), while other dimensions are independent and
cannot be aggregated, (e.g., rating and distance). Thus,
there is a real need for supporting multi-objective queries
as a compromise between top-k and skyline queries. In a
straightforward way, multi-objective queries would perform
an aggregation over dependent dimensions followed by a skyline over all independent dimensions. Due to its practicality,
it is challenging to find more efficient ways to support multiobjective queries. For example, performing the aggregation
and skyline operations within one scan rather than treating
them as two different independent steps.
In addition, we contend that CareDB should be able to
handle several other multi-objective preference evaluation
methods. Examples of these methods include, but are not
limited to, top-k domination [39], k-dominance [5], or kfrequency [6]. These alternative preference methods aim
to improve upon skyline and top-k methods by proposing
different criteria defining how one data object is preferred
to another. The challenge in supporting myriad different
preference methods is to create a sustainable implementation inside the database query processor. A possible solution is to implement each preference method as a customized
operator in the database. For CareDB, we propose an alternative solution: the creation of a generalized extensible
query evaluation framework. Such a framework would be
implemented inside the query processor, however, unlike a
customized approach, only the general framework touches
the query processor. Meanwhile, each preference method
registers with the framework through the implementation of
extensible functions, that are used by the framework during
preference query processing. The advantages of an extensible approach are (1) Sustainability: a generalized approach
requires no changes to the query processor when a new preference method is implemented. (2) Flexibility: multiple
preference methods can co-exist inside the query processor,
and any method can be used at query time.
(MAX, Range [l,u])
Overall rating
Dietary rating
Total price
(DB + User context)
(MIN , Range [l,u])
Meal price
Dessert Price
Time needed
Travel distance
Waiting time
Open now
(DB context)
(MIN , Range [l,u])
(Environment +
DB + User context)
(DB + User context)
Figure 3: User Preference Profile.
current travel time). (d) Based on the current time, the
user may want to look for either opened or closed restaurants. Whenever applicable, the user would specify for each
factor whether it needs to be minimized (e.g., travel distance) or maximized (e.g., restaurant rating). Also, a user
may specify a range for each factor (e.g., a lower and upper
bound for price). In fact, this preference profile can handle more robust preference models, such as PreferenceSQL
fuzzy constraints [37] (e.g., Around).
It is important to emphasize that a user may have multiple
preference profiles for different time instances and different
queries. For example, at the lunch time, the user may turn
her profile to ignore the dessert price and lower the time
range, or, at non-meal times, the user may ignore the factor
of whether the restaurant is currently opening or not. Also,
if the user wants to issue a gas station finder query, the user
would have different factors to be considered. The query
rewriting module should be able to take such mapping from
user profiles to various context and write the user queries in
terms of current preferences and context.
Challenge II: Multi-Objective Query
With the flexibility in defining customized user profiles,
location-based queries tend to be multi-objective queries.
For example, a restaurant finder query aims to achieve various objectives, e.g., minimum price, closest distance, and
highest rating. Such objectives may be contradictory in
many cases, e.g., a higher rating restaurant is probably of
higher price than that of a lower rating one. Given multiobjective queries, it is not immediately clear what answer is
“correct”. As a result, the correct answer of context-ware
location-based queries is not crisply defined. For example,
consider three restaurants r1 , r2 , and r3 . Each restaurant is
represented as a triple (p, d, t) where p indicates the restaurant price, d indicates the distance to the restaurant, and t
indicates the restaurant rating. If the three restaurant vectors are r1 = (30, 50, 5), r2 = (40, 60, 4), and r3 = (35, 20, 3),
then, it is not immediately clear which of these restaurants
could be the right query answer as none of them is clearly
cheaper, closer, and has higher rating than all other restaurants. Compare this scenario with the traditional locationbased restaurant finder scenario where the right answer is
crisply defined as the closest restaurant.
In general, there are two extremes for multi-objective
queries: top-k queries and skyline queries. In top-k queries,
all dimensions (i.e., factors that affect the decision) are
Challenge III: Context-Aware Query
In the restaurant finder query with the preference profile
ACM MobiDE'09 - 8th International ACM Workshop on Data Engineering for Wireless and Mobile Access
basis. For example, instead of computing the distances to
all restaurants, we should aim to only compute the distances
for a selective set of restaurants that would have the potential to participate in the final query answer. Such approach
would significantly enhance the query processing by avoiding redundant extensive computations. The context-aware
query optimizer should be able to decide on executing only
parts of the query to prune the search before executing more
expensive modules. For example, in the restaurant finder
query, we may start by doing the aggregation and skyline
operations considering only the price and rating attributes.
Then, we will compute the distance between user location
and restaurants for only those restaurants that appear in the
price/rating skyline. The computed distances will provide
us with a maximum upper bound of the acceptable restaurants. Then, we can issue a range query to get only those
restaurants within the maximum distance.
The context-aware query optimizer should also take into
account sorted attributes and available indexing schemes.
For example, consider the case that there exist two copies
of the restaurant table where one copy is sorted on price,
while the other copy is sorted on rating. In this case, we
will start by computing the skyline based on these two fields
as skyline computations can make use of the sorted nature
of the data to avoid exhaustive data scan. On the other
hand, if there is a spatial index for restaurant locations, this
would indicate that a range query or nearest-neighbor query
can be exploited for search pruning. An important factor
that should be taken into consideration by the context-aware
query optimizer is that traditional attributes (e.g., price and
rating) can be pre-sorted while ad-hoc computed attributes
(e.g., distance) cannot be pre-sorted as they depend mainly
on the current user location, i.e., a dynamic user context.
Figure 4: Execution Flow.
given in Figure 3, four types of operations need to take place:
(1) Filters. The input list of restaurants need to be filtered
to get rid of unqualified restaurants, e.g., restaurants that
are currently closed or their prices/ratings/distances are out
of the user acceptable range. (2) Aggregation. Some factors
may require performing an aggregation before any decision
is taken. Examples of such factors include rating (average all
user ratings), price (add meal and dessert prices), and total
time needed (add travel time and waiting time). (3) Extensive computation. Some factors may require extensive
computations before being considered. A typical example is
computing the distance from the user location to the restaurant. Such computation requires consulting the environmental context to get the current traffic conditions in order to
estimate the shortest route from the user location to the
restaurant location. (4) Skyline computation. Independent
factors (e.g., the total price and total time) need to be entered to a skyline operator to filter out dominated objects
that cannot be in the query answer.
Figure 4 gives the execution flow for the four operations
in the context-aware restaurant finder query. The first step
is to perform an extensive distance computation with all
restaurants to estimate the travel time needed to reach the
restaurant location. Then, an aggregation will be performed
to aggregate the travel time with the average waiting time at
the restaurant. Also, the aggregation will be done to get the
total price (meal plus dessert) and the average rating (general and dietary rating). Following the aggregation, a filter
operation will be performed to get rid of those restaurants
that are outside the rating, price, or time specified range. Finally, a skyline operator (or any other multi-objective preference evaluation method) will be employed to get the skyline
restaurants in terms of rating, price, and time among the
set of qualified restaurants. Notice that if the aggregation
gives only value, e.g., all preference factors can be aggregated together to produce one scoring function, then, the
skyline operator will be replaced by a top-k operator.
It is clear that the execution flow in Figure 4 may not
be the best possible plan. Thus, it is challenging to design
a context-aware query optimizer that could accurately estimate the cost and selectivity of each operation to be able
to decide upon the best execution flow (i.e., query pipeline).
A main objective of the context-aware query optimizer is to
avoid excessive and redundant computations. Computationally bounded operations should be done only on a request
Challenge IV: Context and PreferenceAware Query Operators
As we discussed in previous sections, preference and
context-aware query operators (i.e., top-k, skyline, and any
multi-objective operators) need to be developed so that
context-aware query optimization can take place. In this
section, we discuss the non-trivial task of embedding preference and context-awareness into the core processing of traditional query operators. In particular, we focus on selection,
aggregate and join operators.
Context and Preference-Aware Selection
A main challenge in adding preference and contextawareness to the selection operator is to perform computationally bounded operations on a request basis. The functionality is best related using an example. Figure 5(a) gives
an example of using a skyline selection preference operator,
with a single base table of restaurants with three attributes:
price, rating, and location. In order to realize the preference function of maximizing rating, while minimizing the
price and distance, the location attribute must be translated to distance through extensive computation that consults the current traffic and road network information. To
avoid such expensive computations, CareDB employs several
techniques, we outline two of them: (1) If a nearest-neighbor
index is available to compute the distance, we can use it to
retrieve restaurants in increasing order based on their distance to the user. Thus, the operator can retrieve the first
nearest restaurant, and its distance can be used as a lower
ACM MobiDE'09 - 8th International ACM Workshop on Data Engineering for Wireless and Mobile Access
rating, maximize the taxi service rating, and minimize the
taxi service charge. This example is depicted in Figure 5(b),
with the Restaurant table on the left and the Taxi table on
the right. Note that the taxi table stores which restaurants
it services by a foreign key, thus a one-to-many join is necessary on the RID attribute. If the join and skyline were
isolated, then a complete join operation, producing all six
results, followed by a skyline operation, would be necessary.
In contrast, CareDB provides an integrated solution using a
simple nested loop join with Restaurant as the inner relation.
The first (RID,TID) pair produced would be (1,1). Then,
the next tuple in Taxi with (RID,TID) key (2,1) could instantly be discarded, as its rating (Rate) and service charge
(SrvC) dimensions are dominated by the previously joined
tuple (1,1). Note that the output of the integrated skyline
join contains only three tuples. Furthermore, this example
shows that the skyline operation cannot simply be pushed
before the join to evaluate local skylines on each table, followed by a join to compute the final result. The local skylines are highlighted in gray in Figure 5(b), however, tuple
(TID:6,RID:4) is a final skyline tuple, but not a local skyline
in the Restaurant or Taxi relation.
The challenge behind embedding preference and context
inside the join operation is to prune input tuples that cannot
possibly contribute to the preference answer during the join
operations. In other words, we aim to avoid a straightforward naive query plan, when possible, that involves joining
all input tuples and then performing preference evaluation
over the complete join result. We are currently exploring
an initial technique that computes a set of local preference
results for each input table separately. While computing
the local preference results for each table, we use the join
predicate to exploit the fact that certain local tuples cannot possibly be preference answers when joined with their
counterpart tuple in the other table. Thus, we mark the potential of each local preference answer to be a global one, i.e.,
appear at the final result. The main motivation is that the
join operators are likely to be non-reductive, i.e., the output
size of the join operator is larger than its input size. Thus,
early pruning is a way to: (1) reduce the cost of the join and
(2) reduce the cost of any final preference evaluation that
marks join result tuples as final preference answers. Making
the join operator preference-aware would greatly affect the
overall query performance.
RID: (2, 3)
Figure 5: Operator Examples
bound to prune candidate skylines in that attribute. In Figure 5(a), the restaurant with RID 2 is considered a skyline as
it is the closest in the distance dimension. Thus, restaurant
RID 1 can be eliminated as it is dominated in the price and
rating dimension, and its distance need not be computed
as its value is definitely less than the distance of restaurant
with RID 2. Similarly, RID 3 is a skyline and RID 4 can
be eliminated without a distance computation as it matches
RID 3 in price and rating dimensions, but is guaranteed not
to be closer than RID 3. (2) If no such distance index is
available, the selection operator can compute a sub-skyline
using the price and rating attributes, perform distance computations on these select restaurants, and use the largest
computed distance in the price/rating skyline as an upper
bound. A range query can then be issued using the upper
bound distance to retrieve all restaurants that can possibly
exist in the final answer, and a final skyline evaluation will
be executed. This approach avoids distance computations
on both (a) restaurants not in the price/rating skyline and
(b) restaurants not within the upper distance range.
Context and Preference-Aware Aggregation
The case of having an aggregate operator followed by a
skyline operator is natural. For example, as we have mentioned in Section 3.2, a multi-objective operator can be represented as an aggregate operator followed by a skyline operator. As multi-objective queries are natural in context-aware
environments, it is essential to avoid having two separate
operators for aggregation and skyline. Thus, it is challenging to embed context-awareness into the traditional aggregate operators. By doing so, we can avoid multiple scans
of the same data sets. For example, the aggregation process requires scanning through the whole data while similar
functionality will be needed for the skyline. If we embed
the aggregation process within the skyline computation, we
will end up avoiding a lot of redundant processing and eliminating several tuples from being considered for the skyline
Challenge V: Context-Aware Continuous
In context-aware environments, continuous queries are
ubiquitous where the surrounding context is continuously
changing. As has been indicated in the system architecture (Figure 2), each type of context (i.e., user, database,
and environment context) has a dynamic component. Such
a dynamic component raises new challenges in supporting context-aware continuous location-based queries. Unlike traditional continuous location-based queries where the
query answer can be continuously changing with the movement of query object or objects of interest, in context-aware
continuous queries, the query answer may change even if neither the query object nor objects of interest have changed
their locations. Such a case can take place if the underlying
context has changed. For example, consider the restaurant
finder continuous query. Even if the query issuer did not
change her location, the answer may continuously change
Context and Preference-Aware Join
In many cases, multi-objective evaluation needs to be performed on attributes in multiple tables, necessitating a join.
The join operator is usually an expensive database operator
that is non-reductive, i.e., the output size is larger than the
input size. Thus, keeping multi-objective preference evaluation (e.g., skyline) and join operations in isolation is inefficient compared to an integrative solution. Consider an
example where a user wants to have dinner at a restaurant
and then use a taxi to get home. Preferences for this night
out could be to minimize dinner price, maximize restaurant
ACM MobiDE'09 - 8th International ACM Workshop on Data Engineering for Wireless and Mobile Access
due to the change in traffic conditions (environment context), restaurant waiting time (database context), or user
available time (user context). It is challenging to accommodate such frequently changing environment when reporting
and updating the query answer.
Another challenge in context-aware continuous queries is
to support an adaptive query optimizer module. Continuous queries tend to reside at the system for long times (e.g.,
hours or days). However, the query optimizer can select
the best query plan only based on the context at the query
submission time. As time goes by, the initial context may
change dramatically making the initial query plan suboptimal. Thus, a set of re-optimization techniques should be
deployed to continuously monitor the performance of the
existing query plan with respect to the currently changing
context and/or preferences. In addition, the query optimizer
would always look for the optimal plan with the change of
context. Once a better plan than the current one is found,
then it is up to the query optimizer to decide if a switch to
the new plan is needed.
Finally, as in traditional location-based queries, shared
execution is a key for achieving scalable and efficient execution of large numbers of outstanding continuous queries.
However, with context-awareness, there are more chances
to explore new kinds of shared execution, i.e., sharing the
underlying context. For example, if two different users issue context-aware queries that involve distance computation
over certain road network, it is crucial to monitor the change
of overlapped road segments for these two queries together.
Once a change in traffic condition takes place, the change
should propagate to the two users. This is in contrast to
having each query executed independent of the other. In
general, it is challenging to figure out various ways of shared
execution that can make use of the underlying context.
PREFER system [17] incorporates preferences into a single
weighted ranking function where preferred results are generated by finding pre-computed materialized views whose
weight function is similar to the query. PreferenceSQL [21,
37] provides new constructs for expressing preference in
SQL, rules for combining preferences in a cascading or
pareto-accumulation manner, and rules for translating PreferenceSQL into traditional SQL queries.
queries [22, 23, 24] model preferences using a degree of
interest score, where queries are injected with mandatory
and secondary preferences based on this score. The resulting query is built using traditional SQL constructs. AmbientDB [14, 36] provides a middleware layer to integrate
myriad multimedia servers with mobile devices in order to
provide efficient ad-hoc queries in a dynamic mobile environment. Contextual database [33, 34] focuses on modeling contextual preferences, and integrating context into query definitions. CareDB distinguishes itself from all these systems
as it: (a) provides a full-fledged realization of preference and
context-aware databases, (b) goes beyond preference modeling and query rewriting to address processing preferences
and context at the query operator levels, (c) unlike other
systems that build personalization and context-management
modules on-top of existing relational databases, CareDB exploits a built-in approach where the preference and contextawareness is embedded into the core processing of query
operators, and (d) CareDB is equipped with the necessary
modules that support the special characteristics of locationbased servers, e.g., continuous queries and dynamic environments.
Context definitions. There have been several definitions
of context and context-awareness (e.g., see [4, 12, 32]). Most
of these definitions define the context in terms of examples
with special emphasis on the location context. Similarly,
there have been several definitions of context-aware applications that include various synonymous, e.g., adaptive applications [3], reactive applications [9], responsive applications [13], situated applications [18], contented-sensitive applications [31], and environment directed applications [15].
In this paper, we stick with the most formal definitions given
by [11] where context is defined as “any information that can
be used to characterize the situation of an entity. An entity
is a person, place, or object that is considered relevant to
the interaction between a user and an application, including
the user and application themselves” while a context-aware
system is defined as “A system that uses context to provide
relevant information and/or services to the user, where relevancy depends on the user’s task”.
Location-based systems. With the explosive growth
of location-based services, several systems have been developed to provide database support for location-based queries.
These systems include DOMINO [38], SECONDO [16], and
PLACE [28, 30]. DOMINO [38] provides several locationbased features on top of existing DBMS’s including dynamic
attributes, a spatial and temporal query language, indexing,
and uncertainty management. SECONDO [16] is an extensible database system, built to support a plethora of nonstandard applications, e.g., location-based services, through
algebra modules. The PLACE server [28, 30] provides the
first built-in approach to support location-based services
through specialized location-based query operators inside
database management systems. CareDB distinguishes itself
from all of these systems as it is the first to go beyond consideration of only the “location” context. CareDB considers
user preferences, and other types of static and dynamic context that include environment, user, and database context.
Furthermore, similar to PLACE, CareDB exploits a built-in
Preference and context in databases. Following
several theoretical works for expressing user preferences in
database systems [2, 7, 8, 25], recent systems have been
developed to include preference and context in databases.
Examples of these systems include PREFER [17], PreferenceSQL [21, 37], Personalized queries [22, 23, 24], AbmientDB [14, 36], and contextual database [33, 34]. The
In this paper, we have discussed the rigidness in current location-based applications that provide services based
only on the location context while ignoring various forms of
user preferences and surrounding context. To overcome such
rigidness, we introduced the system architecture of a Context and Preference-Aware Location-based Database Server
(CareDB, for short), currently under development at University of Minnesota, that delivers personalized services to
its customers based on the surrounding context. CareDB
tailors its functionalities and services based on the context
of each customer. We have identified three categories of
context that should be taken into account when supporting location-based queries, namely, user context (e.g., lo-
ACM MobiDE'09 - 8th International ACM Workshop on Data Engineering for Wireless and Mobile Access
cation, budget, and dietary restrictions), database-specific
context (e.g., restaurant rating, waiting line, and cuisine),
and environmental context (e.g., weather and traffic conditions). Within the framework of CareDB, we have discussed five main challenges that need to be addressed by
the research community in order to have a practical realization of context-aware location-based services. The five
challenges are: (1) Designing a user profile and mapping
it into context parameters, (2) Supporting multi-objective
queries in a practical manner, also going beyond the notion of supporting only top-k and skyline queries, (3) Building context-aware query optimizers that take into consideration the surrounding environment when choosing the best
query plan, (4) Building context-aware query operators that
embed context-awareness into the core processing of traditional query operators (e.g., aggregate and join operators),
and (5) Enabling efficient and scalable execution of contextaware continuous queries that takes into consideration the
frequent changes of the underlying context in addition to
the frequent changes of query and object locations.
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