Data Retrieval Problem Research Articles

Real-Time Data Retrieval With Multiple Availability Intervals in CPS Under Freshness Constraints

Maintaining the temporal validity of real-time data in cyber-physical systems (CPSs) is of critical importance to ensure correct decision making and appropriate system operation. Most existing work on real-time data retrieval assumes that the real-time data under study are always available, and the developed scheduling algorithms mainly focus on making real-time decisions while meeting the temporal validity (freshness) constraints. This assumption, however does not hold in many real-life CPS applications with intermittent data availability, such as in energy harvesting-based sensing systems. In this paper, we study the multi-interval availability-constrained fresh data retrieval (MAFDR) problem, which aims to retrieve all required real-time data on time for a set of decision tasks while taking both the temporal validity and data availability constraints into consideration. We present the formulation of the MAFDR problem and study its complexity under different settings. For the scenario of single decision task with unit-size data retrieval time, we propose a polynomial-time optimal data retrieval algorithm, which comprises a task finish time selection phase and an optimal retrieval schedule construction phase. For the general scenario of multiple decision tasks with nonunit-size data retrieval time, we provide an integer linear programming formulation for the MAFDR problem and propose a fast heuristic algorithm based on max flow. The effectiveness of the proposed algorithms has been validated through extensive experiments by comparing to the optimal solution and the state-of-the-art approach.

Efficient Approximation Algorithms for Multi-Antennae Largest Weight Data Retrieval

In a mobile network, wireless data broadcast over $m$ channels (frequencies) is a powerful means for distributed dissemination of data to clients who access the channels through multi-antennae equipped on their mobile devices. The $\delta$-antennae largest weight data retrieval ($\delta$ALWDR) problem is to compute a schedule for downloading a subset of data items that has a maximum total weight using $\delta$ antennae in a given time interval. In this paper, we propose a ratio $1-\frac{1}{e}-\epsilon$ approximation algorithm for the $\delta$-antennae largest weight data retrieval ($\delta$ALWDR) problem that has the same ratio as the known result but a significantly improved time complexity of $O(2^{\frac{1}{\epsilon}}\frac{1}{\epsilon}m^{7}T^{3.5}L)$ from $O(\epsilon^{3.5}m^{\frac{3.5}{\epsilon}}T^{3.5}L)$ when $\delta=1$ \cite{lu2014data}. To our knowledge, our algorithm represents the first ratio $1-\frac{1}{e}-\epsilon$ approximation solution to $\delta$ALWDR for the general case of arbitrary $\delta$. To achieve this, we first give a ratio $1-\frac{1}{e}$ algorithm for the $\gamma$-separated $\delta$ALWDR ($\delta$A$\gamma$LWDR) with runtime $O(m^{7}T^{3.5}L)$, under the assumption that every data item appears at most once in each segment of $\delta$A$\gamma$LWDR, for any input of maximum length $L$ on $m$ channels in $T$ time slots. Then, we show that we can retain the same ratio for $\delta$A$\gamma$LWDR without this assumption at the cost of increased time complexity to $O(2^{\gamma}m^{7}T^{3.5}L)$. This result immediately yields an approximation solution of same ratio and time complexity for $\delta$ALWDR, presenting a significant improvement of the known time complexity of ratio $1-\frac{1}{e}-\epsilon$ approximation to the problem.

Improved periodic data retrieval in asynchronous rings with a faulty host

The exploration problem has been extensively studied in unsafe networks containing malicious hosts of a highly harmful nature, called black holes, which completely destroy mobile agents that visit them. In a recent work, Královič and Miklík (SIROCCO 2010, LNCS 6058, pp. 157–167) [20] considered various types of malicious host behavior in the context of the Periodic Data Retrieval problem in asynchronous ring networks with exactly one malicious host. In this problem, a team of initially co-located agents must report data from all safe nodes of the network to the homebase, infinitely often. The malicious host can choose whether to kill visiting agents or allow them to pass through (gray hole). In another variation of the model, the malicious host can, in addition, alter its whiteboard contents in order to deceive visiting agents. The goal is to design a protocol for Periodic Data Retrieval using as few agents as possible.In this paper, we present the first nontrivial lower bounds on the number of agents for Periodic Data Retrieval in asynchronous ring networks. Specifically, we show that at least 4 agents are needed when the malicious host is a gray hole, and at least 5 agents are needed when the malicious host whiteboard is unreliable. This improves the previous lower bound of 3 in both cases and answers an open question posed in the aforementioned paper.On the positive side, we propose an optimal protocol for Periodic Data Retrieval in asynchronous rings with a gray hole, which solves the problem with only 4 agents. This improves the previous upper bound of 9 agents and settles the question of the optimal number of agents in the gray-hole case. Finally, we propose a protocol with 7 agents when the whiteboard of the malicious host is unreliable, significantly improving the previously known upper bound of 27 agents. Along the way, we set forth a detailed framework for studying networks with malicious hosts of varying capabilities.

Heuristic Algorithm for Efficient Data Retrieval Scheduling in the Multichannel Wireless Broadcast Environments

Wireless data broadcast is an efficient way of disseminating data to users in the mobile computing environments. From the server’s point of view, how to place the data items on channels is a crucial issue, with the objective of minimizing the average access time and tuning time. Similarly, how to schedule the data retrieval process for a given request at the client side such that all the requested items can be downloaded in a short time is also an important problem. In this paper, we investigate the multi-item data retrieval scheduling in the push-based multichannel broadcast environments. The most important issues in mobile computing are energy efficiency and query response efficiency. However, in data broadcast the objectives of reducing access latency and energy cost can be contradictive to each other. Consequently, we define a new problem named Minimum Cost Data Retrieval Problem (MCDR) and Large Number Data Retrieval (LNDR) Problem. We also develop a heuristic algorithm to download a large number of items efficiently. When there is no replicated item in a broadcast cycle, we show that an optimal retrieval schedule can be obtained in polynomial time.

On-line Data Retrieval Algorithm with Restart Strategy in Wireless Networks

Data retrieval is an efficient scheme for data dissemination in mobile computing, and its primary goal is to improve the waiting time and save the energy of mobile devices. Existing data retrieval algorithms are developed at the basis of a-priori knowledge of wireless data broadcast. In practical, they are not suitable for on-line data retrieval where the knowledge is not known. In this paper, we study on-line data retrieval problem with Las Vegas strategy of randomized algorithm, called as LVDR, and reduce the competitive rate of this problem, , where is the number of channels and is the length of broadcast cycle. Thus, we observe that different schemes of choosing channel will generate the different efficiencies in randomized algorithm. Therefore, we propose another scheme introduced restart strategy to execute the other retrieval sequences of choosing channels when the client may not retrieve the requested data item in certain time, called as LVDR-RS. In addition, we adopt Markov chain to predict the optimal restart cycle for each requested data item. Through analysis, we obverse that the competitive rate is closely related with the number of restarts and restart cycle

Efficient data retrieval algorithms for multiple requests in MIMO wireless networks

SummaryGiven a set of multiple channels, a set of multiple requests, where each request contains multiple requested data items and a client equipped with multiple antennae, the multi‐antenna‐based multirequest data retrieval problem (DRMR‐MA) is to find a data retrieval sequence for downloading all data items of the requests allocated to each antenna, such that the maximum access latency of all antennae is minimized. Most existing approaches for the data retrieval problem focus on either single antenna or single request and are hence not directly applicable to DRMR‐MA for retrieving multiple requests. This paper proposes two data retrieval algorithms that adopt two different grouping schemes to solve DRMR‐MA so that the requests can be suitably allocated to each antenna. To find the data retrieval sequence of each request efficiently, we present a data retrieval scheme that converts a wireless data broadcast system to a special tree. Experimental results show that the proposed scheme is more efficient than other existing schemes. Copyright © 2014 John Wiley & Sons, Ltd.

Tree-based data retrieval algorithm for multi-item request with deadline in wireless networks

The data retrieval problem in wireless communication has attracted increasing attentions in many applications such as electronic information sharing and e-business. However, most existing studies do not consider how to retrieve the requested data items in their deadlines. In this paper, we discuss the so-called deadline-based multi-item data retrieval problem, which aims at finding an access pattern for retrieving requested data items in certain deadlines such that the access latency and data miss rate are minimized. We propose two algorithms, named EDFS and UR, to deal with the above problem. The EDFS algorithm gives priority to retrieve the requested data item with the least deadline, while the UR algorithm introduces the urgent rate that is computed by the broadcast time and deadline of requested data item for further reducing the access latency and data miss ratio. Moreover, we design a tree-based data retrieval algorithm that finds the longest downloading sequence of data items in one broadcast cycle and is applied to EDFS and UR for finding data retrieval sequence of requested data items. The efficiency of our proposed schemes has been validated by the extensive experiments.

DSMC: A Novel Distributed Store-Retrieve Approach of Internet Data Using MapReduce Model and Community Detection in Big Data

The processing of big data is a hotspot in the scientific research. Data on the Internet is very large and also very important for the scientific researchers, so the capture and store of Internet data is a priority among priorities. The traditional single-host web spider and data store approaches have some problems such as low efficiency and large memory requirement, so this paper proposes a big data store-retrieve approach DSMC (distributed store-retrieve approach using MapReduce model and community detection) based on distributed processing. Firstly, the distributed capture method using MapReduce to deduplicate big data is presented. Secondly, the storage optimization method is put forward; it uses the hash functions with light-weight characteristics and the community detection to address the storage structure and solve the data retrieval problems. DSMC has achieved the high performance of large web data comparison and storage and gets the efficient data retrieval at the same time. The experimental results show that, in the Cloudsim platform, comparing with the traditional web spider, the proposed DSMC approach shows better efficiency and performance.

Source Free Transfer Learning for Text Classification

Transfer learning uses relevant auxiliary data to help the learning task in a target domain where labeled data is usually insufficient to train an accurate model. Given appropriate auxiliary data, researchers have proposed many transfer learning models. How to find such auxiliary data, however, is of little research so far. In this paper, we focus on the problem of auxiliary data retrieval, and propose a transfer learning framework that effectively selects helpful auxiliary data from an open knowledge space (e.g. the World Wide Web). Because there is no need of manually selecting auxiliary data for different target domain tasks, we call our framework Source Free Transfer Learning (SFTL). For each target domain task, SFTL framework iteratively queries for the helpful auxiliary data based on the learned model and then updates the model using the retrieved auxiliary data. We highlight the automatic constructions of queries and the robustness of the SFTL framework. Our experiments on 20NewsGroup dataset and a Google search snippets dataset suggest that the framework is capable of achieving comparable performance to those state-of-the-art methods with dedicated selections of auxiliary data.

Objective development of applied information technology collection business information in INTERNET

This article examines the search, collection and processing business information. The main problem is a search for sources of business information. The main groups of sources were identified: legislation, reporting, infrastructure, media and Internet. Internet today is the main source of any kind of information, but there is still the actual problem of data retrieval, that meets the criteria of quality business information. This paper analyzes the requirements for such sources. The criteria for the quality of business information have been identified, among which the most important are accuracy, timeliness, completeness, verifiability, fairness, etc. The detailed analysis of the enterprise data flows resulted in the formulation of information technologies development for the collection and business information storage problem, which would take into account all the basic information in the enterprise, as well as additional sources. The basic requirements that must be met by developed system: adaptability, portability, usability, etc. The present paper identifies the stages of collecting information, set goals and objectives for the solutions. For each task of collecting and storing business information from a variety of sources have been defined the mathematical tools for its program implementation

Information extraction from narrative data

Interesting and exciting work is being done in the medical field to advance clinical decision-support (CDS) technology. One major focus of research is finding ways to access relevant information from a patient’s electronic health record (EHR) and linking that data to CDS tools. The two types of information found in the EHR are coded data and narrative (non-coded) data. Coded data contain structured data elements that are easily queried, such as International Classification of Diseases codes, laboratory values, and medication names. For example, searching an EHR to determine if a patient has taken simvastatin might be as simple as entering the drug name as a search term. However, much of the desired knowledge in the EHR may be, in effect, locked away in narrative text, from which it is more difficult to access data relevant to a particular information need. Narrative text is written in free form (e.g., phrases or sentences in the body of a clinical note). When narrative data such as chart notes are entered into the record, data-retrieval problems arise in relation to the use of synonyms and jargon, misspellings, multiple abbreviations of the same term, and differences between providers in documentation style. Natural language processing (NLP), a method of automated data extraction, holds the potential to unlock the data of interest contained within narrative text.

Algebraic data retrieval algorithms for multi-channel wireless data broadcast

Wireless data broadcast is an important data dissemination method for distributing public information to mobile users. Due to the exponentially increasing number of mobile network users, it is necessary to develop efficient data retrieval protocols for end users to download data items effectively. In this paper, we concentrate on investigating scheduling algorithms for retrieving a set of data items from a multichannel wireless data broadcast system. As we know, the most important issues in mobile computing are energy efficiency and query response efficiency. However, in data broadcast the objectives of reducing access latency and energy cost can be contradictive to each other. Consequently, we define a new problem named Minimum Constraint Data Retrieval Problem (MCDR). We prove that MCDR is NP-hard, and then show a fixed parameter tractable algorithm which can balance two factors together. It has computational time O(2k(hnt)O(1)), where n is the number of channels, k is the number of required data items, t is the maximal time slot, and h is the maximal number of channel switches.

Using an interval graph approach to efficiently solve the housing benefit data retrieval problem

Rappos and Thompson use a set covering formulation and a commercial software package to solve the problem of trying to minimize the number of data sets that have to be read in retrieving all new housing benefit (HB) data entries for a fixed period of time. In this paper, we show that determining the minimum number of data sets that have to be read in retrieving all new HB data entries for a fixed period of time can be solved by finding a minimum size clique cover for an interval graph. Since it is well-known that a greedy algorithm finds a guaranteed minimum size clique cover for an interval graph, this approach will be more efficient than a set covering approach. Finally, it is obvious that this interval graph formulation and greedy algorithm solution approach is applicable to other data retrieval problems.

Metric-space search in bioinformatics

Of the many problems in biological data retrieval, the problem of biological sequence retrieval has the highest profile. The standard solution to this problem, BLAST, has ascended, like google , to become synonymous with search. Also like Google, BLAST leverages statistical properties as heuristics to create a good user experience. Ironically, many early biological sequence similarity efforts explicitly sought to model evolutionary distance as a metric-distance. Recent interest in metric-index methods has rekindled these early directions. A review of these efforts provides an opportunity to characterize the challenges and opportunities in similarity search of biological data.

TF–DF indexing for mocap data segments in measuring relevance based on textual search queries

Many techniques have been proposed to address the problem of mocap data retrieval by using a short motion as input, and they are commonly categorized as content-based retrieval. However, it is difficult for users who do not have equipments to create mocap data samples to take advantage of them. On the contrary, simple retrieval methods which only require text as input can be used by everyone. Nevertheless, not only that it is not clear how to measure mocap data relevance in regard to textual search queries, but the search results will also be limited to the mocap data samples, the annotations of which contain the words in the search query. In this paper, the authors propose a novel method that builds on the TF (term frequency) and IDF (inverse document frequency) weights, commonly used in text document retrieval, to measure mocap data relevance in regard to textual search queries. We extract segments from mocap data samples and regard these segments as words in text documents. However, instead of using IDF which prioritizes infrequent segments, we opt to use DF (document frequency) to prioritize frequent segments. Since motions are not required as input, everybody will be able to take advantage of our approach, and we believe that our work also opens up possibilities for applying developed text retrieval methods in mocap data retrieval.

Rate–Distortion Approach to Databases: Storage and Content-Based Retrieval

This paper investigates the relationship between rate-distortion theory and efficient content-based data retrieval from high-dimensional databases. We consider database design as the encoding of a data object sequence, and retrieval from the database as the decoding of the sequence using side information (i.e., the query) available only at the decoder. We show that, in this setting, the optimal asymptotic tradeoff between the search time R/sub s/ (bits per data object read from the storage device) and the expected search accuracy D/sub s/ (relevance of the retrieved data set) is given by the Wyner-Ziv solution with a side-information-dependent distortion measure. Moreover, the data indexing and retrieval problem is, in general, inseparable from the data compression problem. Data items selected by the search procedure, which can be stored in the disk with a limited total rate of R/sub r/ /spl ges/ R/sub s/, need to be presented at a prescribed expected reconstruction quality D/sub r/. This is, hence, a problem of scalable source coding or refinement, albeit with differing layer distortion measures to quantify search and reconstruction quality, respectively. We derive a single-letter characterization of all achievable quadruples {R/sub s/,R/sub r/,D/sub s/,D/sub r/}, and prove conditions for successive refinability without rate loss. Finally, we show that the special case D/sub s/=D/sub r/=0 is nontrivial and of practical interest in this context, as it can impose acceptable search and reconstruction qualities for each individual data item and for the entire query space with high probability, in contradistinction with standard average distortion requirements. The region of achievable {R/sub s/,R/sub r/} is obtained by adapting Rimoldi's characterization to a new regular scalable coding problem.

Optical Data Storage-A New Frontier

The impact of technology on information began the day catalogue card was devised to store the bibliographic details of a book. Initially, the size of a catalogue card used to be 5x3 in. it was increased to 6x4 in. as need .was felt to record more information. With rapid increase in the volume of scientific literature, microform replaced paper for storing information. With the advent of computers, it appeared that the problem of data storage and retrieval of information had been solved, but soon it was realized that even magnetic medium used for storing information was not sufficient, and floppy disc and hard disk offsetting higher capacity and fast retrieval were developed. But the race between the volume of information generated and the medium for its storage did noted there. Thus, versatile medium of mass storage in the form of optical disc was found.

A probabilistic framework for semantic video indexing, filtering, and retrieval

Semantic filtering and retrieval of multimedia content is crucial for efficient use of the multimedia data repositories. Video query by semantic keywords is one of the most difficult problems in multimedia data retrieval. The difficulty lies in the mapping between low-level video representation and high-level semantics. We therefore formulate the multimedia content access problem as a multimedia pattern recognition problem. We propose a probabilistic framework for semantic video indexing, which call support filtering and retrieval and facilitate efficient content-based access. To map low-level features to high-level semantics we propose probabilistic multimedia objects (multijects). Examples of multijects in movies include explosion, mountain, beach, outdoor, music etc. Semantic concepts in videos interact and to model this interaction explicitly, we propose a network of multijects (multinet). Using probabilistic models for six site multijects, rocks, sky, snow, water-body forestry/greenery and outdoor and using a Bayesian belief network as the multinet we demonstrate the application of this framework to semantic indexing. We demonstrate how detection performance can be significantly improved using the multinet to take interconceptual relationships into account. We also show how the multinet can fuse heterogeneous features to support detection based on inference and reasoning.

Image analysis for digital media applications

In this broad-based overview, we review image analysis for three classes of digital media applications: traditional signal processing related procedures, including image enhancement, restoration and compression; relatively new media creation and data retrieval problems, including computer animation and multimedia data retrieval; and pattern recognition, including document imaging and electronic books.

Efficient Near Neighbor Searching Using Multi-Indexes for Content-Based Multimedia Data Retrieval

Many content-based multimedia data retrieval problems can be transformed into the near neighbor searching problem in multidimensional feature space. An efficient near neighbor searching algorithm is needed when developing a multimedia database system. In this paper, we propose an approach to efficiently solve the near neighbor searching problem. In this approach, along each dimension an index is constructed according to the values of feature points of multimedia objects. A user can pose a content-based query by specifying a multimedia query example and a similarity measure. The specified query example will be transformed into a query point in the multi-dimensional feature space. The possible result points in each dimension are then retrieved by searching the value of the query point in the corresponding dimension. The sets of the possible result points are merged one by one by removing the points which are not within the query radius. The resultant points and their distances from the query point form the answer of the query. To show the efficiency of our approach, a series of experiments are performed to compare with the related approaches.