Khalid Saleem

PORSCHE: Performance ORiented SCHEma mediation

Semantic matching of schemas in heterogeneous data sharing systems is time consuming and error prone. Existing mapping tools employ semi-automatic techniques for mapping two schemas at a time. In a large-scale scenario, where data sharing involves a large number of data sources, such techniques are not suitable. We present a new robust automatic method which discovers semantic schema matches in a large set of XML schemas, incrementally creates an integrated schema encompassing all schema trees, and defines mappings from the contributing schemas to the integrated schema. Our method, PORSCHE (Performance ORiented SCHEma mediation), utilises a holistic approach which first clusters the nodes based on linguistic label similarity. Then it applies a tree mining technique using node ranks calculated during depth-first traversal. This minimises the target node search space and improves performance, which makes the technique suitable for large-scale data sharing. The PORSCHE framework is hybrid in nature and flexible enough to incorporate more matching techniques or algorithms. We report on experiments with up to 80 schemas containing 83,770 nodes, with our prototype implementation taking 587s on average to match and merge them, resulting in an integrated schema and returning mappings from all input schemas to the integrated schema. The quality of matching in PORSCHE is shown using precision, recall and F-measure on randomly selected pairs of schemas from the same domain. We also discuss the integrity of the mediated schema in the light of completeness and minimality measures.

Performance oriented schema matching

Semantic matching of schemas in heterogeneous data sharing systems is time consuming and error prone. Existing mapping tools employ semi-automatic techniques for mapping two schemas at a time. In a large-scale scenario, where data sharing involves a large number of data sources, such techniques are not suitable. We present a new robust mapping method which creates a mediated schema tree from a large set of input XML schema trees and defines mappings from the contributing schema to the mediated schema. The result is an almost automatic technique giving good performance with approximate semantic match quality. Our method uses node ranks calculated by pre-order traversal. It combines tree mining with semantic label clustering which minimizes the target search space and improves performance, thus making the algorithm suitable for large scale data sharing. We report on experiments with up to 80 schemas containing 83,770 nodes, with our prototype implementation taking 587 seconds to match and merge them to create a mediated schema and to return mappings from input schemas to the mediated schema.

Solar Energy Harvesting and Management in Wireless Sensor Networks

Wireless networks comprise of small devices that are typically deployed in environments where paucity of energy seriously restricts essential operations. The energy source of these devices decreases very quickly during continuous operation and it is pivotal to replace or recharge frequently the power sources. Sometimes, it is very difficult to perform these functions through conventional methods. One attractive solution to this problem is the use of the energy, scattered around us in the environment. The availability of energy from the environment is random and uncertain. In this paper, we present a model, schematically and analytically, for solar energy harvesting with appropriate energy management. We provide analysis and simulations for a solar cell for standard and different irradiance levels. The power of the storage device is also simulated for different times of the day. The proposed model not only scavenges the energy but also assures the connectivity of the network by optimizing the energy consumption.

Automatic Extraction of Structurally Coherent Mini-Taxonomies

Today, ontologies are being used to model a domain of knowledge in semantic web. OWL is considered to be the main language for developing such ontologies. It is based on the XML model, which inherently follows the hierarchical structure. In this paper we demonstrate an automatic approach for emergent semantics modeling of ontologies. We follow the collaborative ontology construction method without the direct interaction of domain users, engineers or developers. A very important characteristic of an ontology is its hierarchical structure of concepts. We consider large sets of domain specific hierarchical structures as trees and apply frequent sub-tree mining for extracting common hierarchical patterns. Our experiments show that these hierarchical patterns are good enough to represent and describe the concepts for the domain ontology. The technique further demonstrates the construction of the taxonomy of domain ontology. In this regard we consider the largest frequent tree or a tree created by merging the set of largest frequent sub-trees as the taxonomy. We argue in favour of the trustabilty for such a taxonomy and related concepts, since these have been extracted from the structures being used with in the specified domain.

Energy Harvesting and Management in Wireless Networks for Perpetual Operations

Ad hoc wireless networks are self-generating and self-organizing networks consisting of mobile and static nodes, which are small and have limited power resources. In a typical setup, these nodes communicate with each other through wireless medium and may act as source, destination and/or relaying nodes. As the power of the remote nodes is depleted very quickly, it is important to have a renewable energy source to support the network operations and increase lifetime. The availability of energy from the environment is unpredictable, random and uncertain, therefore energy harvesting with appropriate management plays an important role in continuous operations of ad hoc networks. In this paper, an energy harvesting and management model is presented for ad hoc networks. Along with harvesting energy, the proposed model ensures the connectivity requirements of the network for its perpetual operation.

Complex Schema Match Discovery and Validation through Collaboration

In this paper, we demonstrate an approach for the discovery and validation of n:m schema match in the hierarchical structures like the XML schemata. Basic idea is to propose an n:m node match between children (leaf nodes) of two matching non-leaf nodes of the two schemata. The similarity computation of the two non-leaf nodes is based upon the syntactic and linguistic similarity of the node labels supported by the similarity among the ancestral paths from nodes to the root. The n:m matching proposition is then validated with the help of the mini-taxonomies: hierarchical structures extracted from a large set of schema trees belonging to the same domain. The technique intuitively supports the collective intelligence of the domain users, indirectly collaborating for the validation of the complex match propositions.

Snow Leopard Recognition Using Deep Convolution Neural Network

The paper describes the use of Deep Convolution Neural Networks (DCNN) for the recognition of Snow Leopards, from a data set of photos taken in the wild. The data set comprises of 1500 images, captured in the Himalayas using motion sensing cameras. The images contain numerous living species, ranging from a butterfly to a human being, other than Snow Leopard. For the training phase we divided the data set into two classes, Snow Leopard and Other Animals. The Snow Leopard class contains photos showing more than one animal, from different angles, having different sizes, body parts because of distance from camera and several backgrounds. The photos are converted to 200 x 200, grey scale images in the preprocessing phase. A 5 layer DCCN, constituted of 3 convoluted and 2 fully connected layers, is employed for the experimental setup. Rectified Liner Units (ReLU) is used as the activation function in the fully connected layers and softmax function is applied for classification. The evaluation of the system shows an overall 91% accuracy, along with sensitivity of 0.90 and specificity of 0.88 for Snow Leopard class identification.

ACO-Discreet: An Efficient Node Deployment Approach in Wireless Sensor Networks

Wireless sensor networks (WSNs) rely on effective deployment of sensing nodes. Efficient sensor deployment with ensured connectivity is a major challenge in WSNs. Several deployment approaches have been proposed in literature to address the connectivity and efficiency of sensor networks. However, most of these works either lack in efficiency or ignore the connectivity issues. In this paper, we propose an efficient and connectivity-based algorithm by modifying the Ant Colony Optimization (ACO) (Liu and He, J Netw Comput Appl 39:310–318, 2014). Traditional ACO algorithms ensure coverage at a high cost and repetitive sensing, which results in resource wastage. Our proposed algorithm reduces the sensing cost with efficient deployment and enhanced connectivity. Simulation results indicate the ability of proposed framework to significantly reduce the coverage cost as well as achieve longer life time for WSNs.

Optimally Compressed Digital Content Delivery Using Short Message Service

Researchers are devising new ways for robust digital content delivery in situations where telecommunication signal strength is very low, especially during natural disasters. In this paper, we present research work targeting two dimensions: (a) We selected the IANA standard for digital content classification, 20 types in 5 categories; applied and compared five different lossless compression schemes (LZW, Huffman coding, PPM, Arithmetic Coding, BWT and LZMA) on these 20 data types; (b) A generic prototype application which encodes (for sending) and decodes (on receiving) the compressed digital content over SMS. Sending digital contents via SMS over satellite communication is achieved by converting digital content into text; apply lossless compression on the text and transmit the compressed text by using SMS. Proposed method does not require Internet Service and also not requires any additional hardware in existing network architecture to transmit digital contents. Results show that overall PPM compression method offers best compression ratio (0.63) among all compression schemes Thus PPM reduces the SMS transmission saving up to 43%, while LZW performs the least with 17.6%.

K-Nearest Temperature Trends: A Method for Weather Temperature Data Imputation

In this paper, we describe a novel method for imputing weather temperature data. The technique discussed is targeted toward unit imputation of a missing surface temperature value, for a specific weather station, on a specific date. The imputation method relies solely on the available daily maximum temperature data set of the weather station. We propose a hybrid approach, K-Nearest Temperature Trends (KNTT) which identifies a cluster of K years, showing nearest temperature trends to that of the year of the missing value date. Next, the missing temperature value of the date is imputed by taking average of the values for the same date of the identified K-Trends years. We used the data set of temperature values from 38 weather stations of Pakistan, spanning over 30 years (1980-2010), for our experiments. We evaluated our methodology by using ME, MAE and RMSE and the results show that our technique imputed correctly, with an error rate less than the standard KNN technique.