PDQ: Proof-driven Query Answering over Web=based Data
Abstract: The data needed to answer queries is often available through Web-based APIs. Indeed, for a given query there may be many Web-based sources which can be used to answer it, with the sources overlapping in their vocabularies, and differing in their access restrictions (required arguments) and cost.
We introduce PDQ (Proof-Driven Query Answering), a system for determining a query plan in the presence of web-based sources. It is: (i) constraint-aware -- exploiting relationships between sources to rewrite an expensive query into a cheaper one, (ii) access-aware -- abiding by any access restrictions known in the sources, and (iii) cost-aware -- making use of any cost information that is available about services.
PDQ takes the novel approach of generating query plans from proofs that a query is answerable. We demonstrate the use of PDQ and its effectiveness in generating low-cost plans.
PDQ: Proof-driven Query Answering over Web=based Data
Abstract: The data needed to answer queries is often available through Web-based APIs. Indeed, for a given query there may be many Web-based sources which can be used to answer it, with the sources overlapping in their vocabularies, and differing in their access restrictions (required arguments) and cost.
We introduce PDQ (Proof-Driven Query Answering), a system for determining a query plan in the presence of web-based sources. It is: (i) constraint-aware -- exploiting relationships between sources to rewrite an expensive query into a cheaper one, (ii) access-aware -- abiding by any access restrictions known in the sources, and (iii) cost-aware -- making use of any cost information that is available about services.
PDQ takes the novel approach of generating query plans from proofs that a query is answerable. We demonstrate the use of PDQ and its effectiveness in generating low-cost plans.
Data Structures are the programmatic way of storing data so that data can be used efficiently
Introduction to DSA
Advantages & Disadvantages
Abstract Data Type (ADT)
Linear Array List
Downloadable Resources
On the Management, Analysis and Simulation of our LifeStepsytheodoridis
Invited talk delivered at Paris Descartes Univ., Seminars on Data Analytics, Paris, 15.10.2015. Link: http://www.mi.parisdescartes.fr/~themisp/seminars/2015-10-22-Theodoridis.html
Stack is a collection based on the principle of adding elements and retrieving them in the opposite order
What is STACK?
Stack Operations
Applications
Built-in Stack
Downloadable Resources
Abstract data types (adt) intro to data structure part 2Self-Employed
Abstract Data type (ADT), Related to DATA STRUCTURE and ALGORITHMS STACK QUEUE ARRAY LINKED LIST ALGORITHMS AND INSERTION DELETION MERGE TRAVERSE MODIFY AND OTHER related operation in the algorithms of stack queue array and linked list as an ADT type
Data Structures are the programmatic way of storing data so that data can be used efficiently
Introduction to DSA
Advantages & Disadvantages
Abstract Data Type (ADT)
Linear Array List
Downloadable Resources
On the Management, Analysis and Simulation of our LifeStepsytheodoridis
Invited talk delivered at Paris Descartes Univ., Seminars on Data Analytics, Paris, 15.10.2015. Link: http://www.mi.parisdescartes.fr/~themisp/seminars/2015-10-22-Theodoridis.html
Stack is a collection based on the principle of adding elements and retrieving them in the opposite order
What is STACK?
Stack Operations
Applications
Built-in Stack
Downloadable Resources
Abstract data types (adt) intro to data structure part 2Self-Employed
Abstract Data type (ADT), Related to DATA STRUCTURE and ALGORITHMS STACK QUEUE ARRAY LINKED LIST ALGORITHMS AND INSERTION DELETION MERGE TRAVERSE MODIFY AND OTHER related operation in the algorithms of stack queue array and linked list as an ADT type
Trajectory Segmentation and Sampling of Moving Objects Based On Representativ...ijsrd.com
Moving Object Databases (MOD), although ubiquitous, still call for methods that will be able to understand, search, analyze, and browse their spatiotemporal content. In this paper, we propose a method for trajectory segmentation and sampling based on the representativeness of the (sub) trajectories in the MOD. In order to find the most representative sub trajectories, the following methodology is proposed. First, a novel global voting algorithm is performed, based on local density and trajectory similarity information. This method is applied for each segment of the trajectory, forming a local trajectory descriptor that represents line segment representativeness. The sequence of this descriptor over a trajectory gives the voting signal of the trajectory, where high values correspond to the most representative parts. Then, a novel segmentation algorithm is applied on this signal that automatically estimates the number of partitions and the partition borders, identifying homogenous partitions concerning their representativeness. Finally, a sampling method over the resulting segments yields the most representative sub trajectories in the MOD. Our experimental results in synthetic and real MOD verify the effectiveness of the proposed scheme, also in comparison with other sampling techniques.
The 3TU.Datacentrum repository of research data hosts datasets as well as other objects representing measuring devices, locations, time periods and the like. Virtually all metadata is in rdf so the repository can be approached as an rdf graph. We will show how this is implemented with Fedora Commons, heavily leaning on rdf queries and xslt2.0. As a result of this architecture, it is relatively easy to make the repository linked-data-enabled by generating OAI/ORE resource maps.
While most of the metadata is rdf, most of the data is in NetCDF. Although not very well known in the library world, this is very popular format in various fields of science and engineering. It comes with its own data server Opendap which offers a rich API to interact with the data. Our repository is therefore a hybrid Fedora + Opendap setup and we will show how the two are integrated into a unified view and how they are kept in sync on ingest.
This was presented at the ELAG conference, Palma de Mallorca 2012.
Vibrant Technologies is headquarted in Mumbai,India.We are the best r programming training provider in Navi Mumbai who provides Live Projects to students.We provide Corporate Training also.We are Best r programming classes in Mumbai according to our students and corporates
R is a programming language and environment commonly used in statistical computing, data analytics and scientific research.
It is one of the most popular languages used by statisticians, data analysts, researchers and marketers to retrieve, clean, analyze, visualize and present data.
Due to its expressive syntax and easy-to-use interface, it has grown in popularity in recent years.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
I summarize requirements for an "Open Analytics Environment" (aka "the Cauldron"), and some work being performed at the University of Chicago and Argonne National Laboratory towards its realization.
Present day, mining of high utility itemsets
especially from transactional databases is required task to
process many transactional operations quick. There are many
methods that are presented for mining high utility itemsets from
transactional datasets are subjected to some serious limitations
such as performance of this methods needs to be investigated in
low memory based systems for mining high utility itemsets from
large transactional datasets and hence needs to address further
as well. Further limitation includes these methods cannot
overcome the screenings as well as overhead of null transactions;
hence, performance degrades eventually. We are analyzing the
new approaches to overcome these limitations such as distributed
programming model for mining business-oriented transactional
datasets, which overcomes the limitations and main memorybased
computing, but also unexpectedly highly scalable in terms
of increasing database size. We have used this approach with
existing UP-Growth and UP-Growth+ with aim of improving
their performances further.
19. Data Structures and Algorithm ComplexityIntro C# Book
In this chapter we will compare the data structures we have learned so far by the performance (execution speed) of the basic operations (addition, search, deletion, etc.). We will give specific tips in what situations what data structures to use. We will explain how to choose between data structures like hash-tables, arrays, dynamic arrays and sets implemented by hash-tables or balanced trees. Almost all of these structures are implemented as part of NET Framework, so to be able to write efficient and reliable code we have to learn to apply the most appropriate structures for each situation.
A SERIAL COMPUTING MODEL OF AGENT ENABLED MINING OF GLOBALLY STRONG ASSOCIATI...ijcsa
The intelligent agent based model is a popular approach in constructing Distributed Data Mining (DDM) systems to address scalable mining over large scale and ever increasing distributed data. In an agent based
distributed system, variety of agents coordinate and communicate with each other to perform the various
tasks of the Data Mining (DM) process. In this study a serial computing mode of a multi-agent system
(MAS) called Agent enabled Mining of Globally Strong Association Rules (AeMGSAR) is presented based
on the serial itinerary of the mobile agents. A Running environment is also designed for the implementation and performance study of AeMGSAR system.
NLP Project: Machine Comprehension Using Attention-Based LSTM Encoder-Decoder...Eugene Nho
Machine comprehension remains a challenging open area of research. While many question answering models have been explored for existing datasets, little work has been done with the newly released MS MARCO dataset, which mirrors the reality much more closely and poses many unique challenges. We explore an end-to-end neural architecture with attention mechanisms for comprehending relevant information and generating text answers for MS MARCO.
NLP Project: Machine Comprehension Using Attention-Based LSTM Encoder-Decoder...
Daedalus
1. R. Ortale (2) , E. Ritacco (2) , N. Pelekis (3) R. Trasarti (1), F. Giannotti (1) , C. Renso (1) , G. Costa (2) , G. Manco (2) , Y. Theodoridis (3) (1) ISTI-CNR , Pisa, Italy (2) ICAR-CNR , Rende (CS), Italy (3) Univerity of Pireus , Athens, Greece The DAEDALUS Framework: Progressive Querying and Mining of Movement Data
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5. The Two Worlds framework Filtering operators : manipulate basic objects. Mining operators : extract properties from samples. K:D M Population operators : detect samples exhibiting properties. P:DxM D
6. From Two Worlds to Daedalus Hermes is the repository of both data and models. Hermes has been extended to represent objects in M-World: Model_TAS, (T-Pattern) The mining operator is realized by calling an external algorithm. The populate operator has been defined on Hermes
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9. Model representation For T-Pattern, a Model_Tas is defined in Hermes as: Sequence of <Region, <Minimum travel time, Maximum travel time>> Model_TAS: VARRAY <SDO_Geometry, <TAU_TLL.interval, TAU_TLL.interval>> <A,<10,30>; B<5,60>; C<nd,nd>> A B c 10,30 5,60
10. The Daedalus system DAEDALUS provides a Data Mining Query Language based on SQL, that includes basic mechanisms for interactive queries on D-World and M-World
11. The Daedalus System Architecture HERMES DMQL query Model_TAS Package MOD Mediator Controller Parser Object Translator Mining Engine T-Pattern Algorithm User Interface TAS Translation Library Moving_point Translation Library Object Store
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Editor's Notes
A flurry of research has covered with spatio-temporal data analysis from different perspectives. The definition of new movement patterns. The development of solutions to algorithmic issues, with which to improve existing pattern-mining schemes Little attention has been paid to the definition of a unifying framework, wherein to set the above pattern-mining tools as specific components of the knowledge discovery process. Knowledge discovery is a multi-step process, that involves data preprocessing, different pattern mining stages and pattern postprocessing.
A flurry of research has covered with spatio-temporal data analysis from different perspectives. The definition of new movement patterns. The development of solutions to algorithmic issues, with which to improve existing pattern-mining schemes Little attention has been paid to the definition of a unifying framework, wherein to set the above pattern-mining tools as specific components of the knowledge discovery process. Knowledge discovery is a multi-step process, that involves data preprocessing, different pattern mining stages and pattern postprocessing.
![Motivation ,[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)