33,919 research outputs found
Effective and Efficient Algorithms for Concise Range Queries
With the advance of wireless communication technology, it is quite common for people to view maps or get related services from the handheld devices, such as mobile phones and PDAs. Range queries, as one of the most commonly used tools, are often posed by the users to retrieve needful information from a spatial database. However, due to the limits of communication bandwidth and hardware power of handheld devices, displaying all the results of a range query on a handheld device is neither communication efficient nor informative to the users. This is simply because that there are often too many results returned from a range query. In view of this problem, we present a novel idea that a concise representation of a specified size for the range query results, while incurring minimal information loss, shall be computed and returned to the user. Such a concise range query not only reduces communication costs, but also offers better usability to the users, providing an opportunity for interactive exploration. The usefulness of the concise range queries is confirmed by comparing it with other possible alternatives, such as sampling and clustering. Unfortunately, we prove that finding the optimal representation with minimum information loss is an NP-hard problem. Therefore, we propose several effective and nontrivial algorithms to find a good approximate result. Extensive experiments on real-world data have demonstrated the effectiveness and efficiency of the proposed techniques
Path prediction and predictive range querying in road network databases
In automotive applications, movement-path prediction enables the delivery of predictive and relevant services to drivers, e.g., reporting traffic conditions and gas stations along the route ahead. Path prediction also enables better results of predictive range queries and reduces the location update frequency in vehicle tracking while preserving accuracy. Existing moving-object location prediction techniques in spatial-network settings largely target short-term prediction that does not extend beyond the next road junction. To go beyond short-term prediction, we formulate a network mobility model that offers a concise representation of mobility statistics extracted from massive collections of historical object trajectories. The model aims to capture the turning patterns at junctions and the travel speeds on road segments at the level of individual objects. Based on the mobility model, we present a maximum likelihood and a greedy algorithm for predicting the travel path of an object (for a time duration h into the future). We also present a novel and efficient server-side indexing scheme that supports predictive range queries on the mobility statistics of the objects. Empirical studies with real data suggest that our proposals are effective and efficien
Four Lessons in Versatility or How Query Languages Adapt to the Web
Exposing not only human-centered information, but machine-processable data on the Web is one of the commonalities of recent Web trends. It has enabled a new kind of applications and businesses where the data is used in ways not foreseen by the data providers. Yet this exposition has fractured the Web into islands of data, each in different Web formats: Some providers choose XML, others RDF, again others JSON or OWL, for their data, even in similar domains. This fracturing stifles innovation as application builders have to cope not only with one Web stack (e.g., XML technology) but with several ones, each of considerable complexity. With Xcerpt we have developed a rule- and pattern based query language that aims to give shield application builders from much of this complexity: In a single query language XML and RDF data can be accessed, processed, combined, and re-published. Though the need for combined access to XML and RDF data has been recognized in previous work (including the W3Cās GRDDL), our approach differs in four main aspects: (1) We provide a single language (rather than two separate or embedded languages), thus minimizing the conceptual overhead of dealing with disparate data formats. (2) Both the declarative (logic-based) and the operational semantics are unified in that they apply for querying XML and RDF in the same way. (3) We show that the resulting query language can be implemented reusing traditional database technology, if desirable. Nevertheless, we also give a unified evaluation approach based on interval labelings of graphs that is at least as fast as existing approaches for tree-shaped XML data, yet provides linear time and space querying also for many RDF graphs. We believe that Web query languages are the right tool for declarative data access in Web applications and that Xcerpt is a significant step towards a more convenient, yet highly efficient data access in a āWeb of Dataā
Efficient Algorithms for Privately Releasing Marginals via Convex Relaxations
Consider a database of people, each represented by a bit-string of length
corresponding to the setting of binary attributes. A -way marginal
query is specified by a subset of attributes, and a -dimensional
binary vector specifying their values. The result for this query is a
count of the number of people in the database whose attribute vector restricted
to agrees with .
Privately releasing approximate answers to a set of -way marginal queries
is one of the most important and well-motivated problems in differential
privacy. Information theoretically, the error complexity of marginal queries is
well-understood: the per-query additive error is known to be at least
and at most
. However, no polynomial
time algorithm with error complexity as low as the information theoretic upper
bound is known for small . In this work we present a polynomial time
algorithm that, for any distribution on marginal queries, achieves average
error at most . This error
bound is as good as the best known information theoretic upper bounds for
. This bound is an improvement over previous work on efficiently releasing
marginals when is small and when error is desirable. Using private
boosting we are also able to give nearly matching worst-case error bounds.
Our algorithms are based on the geometric techniques of Nikolov, Talwar, and
Zhang. The main new ingredients are convex relaxations and careful use of the
Frank-Wolfe algorithm for constrained convex minimization. To design our
relaxations, we rely on the Grothendieck inequality from functional analysis
Better bitmap performance with Roaring bitmaps
Bitmap indexes are commonly used in databases and search engines. By
exploiting bit-level parallelism, they can significantly accelerate queries.
However, they can use much memory, and thus we might prefer compressed bitmap
indexes. Following Oracle's lead, bitmaps are often compressed using run-length
encoding (RLE). Building on prior work, we introduce the Roaring compressed
bitmap format: it uses packed arrays for compression instead of RLE. We compare
it to two high-performance RLE-based bitmap encoding techniques: WAH (Word
Aligned Hybrid compression scheme) and Concise (Compressed `n' Composable
Integer Set). On synthetic and real data, we find that Roaring bitmaps (1)
often compress significantly better (e.g., 2 times) and (2) are faster than the
compressed alternatives (up to 900 times faster for intersections). Our results
challenge the view that RLE-based bitmap compression is best
Survey over Existing Query and Transformation Languages
A widely acknowledged obstacle for realizing the vision of the Semantic Web is the inability
of many current Semantic Web approaches to cope with data available in such diverging
representation formalisms as XML, RDF, or Topic Maps. A common query language is the first
step to allow transparent access to data in any of these formats. To further the understanding
of the requirements and approaches proposed for query languages in the conventional as well
as the Semantic Web, this report surveys a large number of query languages for accessing
XML, RDF, or Topic Maps. This is the first systematic survey to consider query languages from
all these areas. From the detailed survey of these query languages, a common classification
scheme is derived that is useful for understanding and differentiating languages within and
among all three areas
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