Abstract Cloud service providers offer application providers with virtually infinite storage and computing resources, while providing cost-efficiency and various other quality of service (QoS) properties through a storage-as-a-service (StaaS) approach. Organizations also use multi-cloud or hybrid solutions by combining multiple public and/or private cloud service providers to avoid vendor lock-in, achieve high availability and performance, and optimise cost. Indeed cost is one of the important factors for organizations while adopting cloud storage; however, cloud storage providers offer complex pricing policies, including the actual storage cost and the cost related to additional services (e.g., network usage cost). In this article, we provide a detailed taxonomy of cloud storage cost and a taxonomy of other QoS elements, such as network performance, availability, and reliability. We also discuss various cost trade-offs, including storage and computation, storage and cache, and storage and network. Finally, we provide a cost comparison across different storage providers under different contexts and a set of user scenarios to demonstrate the complexity of cost structure and discuss existing literature for cloud storage selection and cost optimization. We aim that the work presented in this article will provide decision-makers and researchers focusing on cloud storage selection for data placement, cost modelling, and cost optimization with a better understanding and insights regarding the elements contributing to the storage cost and this complex problem domain.

The exponential growth of data production emphasizes the importance of database management systems (DBMS) for managing vast amounts of data. However, the complexity of writing Structured Query Language (SQL) queries requires a diverse range of skills, which can be a challenge for many users. Different approaches are proposed to address this challenge by aiding SQL users in mitigating their skill gaps. One of these approaches is to design recommendation systems that provide several suggestions to users for writing their next SQL queries. Despite the availability of such recommendation systems, they often have several limitations, such as lacking sequence-awareness, session-awareness, and context-awareness. In this paper, we propose TRANSQLATION, a session-aware and sequence-aware recommendation system that recommends the fragments of the subsequent SQL query in a user session. We demonstrate that TRANSQLATION outperforms existing works by achieving, on average, 22% more recommendation accuracy when having a large amount of data and is still effective even when training data is limited. We further demonstrate that considering contextual similarity is a critical aspect that can enhance the accuracy and relevance of recommendations in query recommendation systems.

Cloud computing has become an increasingly popular choice for businesses and individuals due to its flexibility, scalability, and convenience; however, the rising cost of cloud resources has become a significant concern for many. The pay-per-use model used in cloud computing means that costs can accumulate quickly, and the lack of visibility and control can result in unexpected expenses. The cost structure becomes even more complicated when dealing with hybrid or multi-cloud environments. For businesses, the cost of cloud computing can be a significant portion of their IT budget, and any savings can lead to better financial stability and competitiveness. In this respect, it is essential to manage cloud costs effectively. This requires a deep understanding of current resource utilization, forecasting future needs, and optimising resource utilization to control costs. To address this challenge, new tools and techniques are being developed to provide more visibility and control over cloud computing costs. In this respect, this paper explores a graph-based solution for modelling cost elements and cloud resources and potential ways to solve the resulting constraint problem of cost optimisation. We primarily consider utilization, cost, performance, and availability in this context. Such an approach will eventually help organizations make informed decisions about cloud resource placement and manage the costs of software applications and data workflows deployed in single, hybrid, or multi-cloud environments.

Prompt-based language models have produced encouraging results in numerous applications, including Named Entity Recognition (NER) tasks. NER aims to identify entities in a sentence and provide their types. However, the strong performance of most available NER approaches is heavily dependent on the design of discrete prompts and a verbalizer to map the model-predicted outputs to entity categories, which are complicated undertakings. To address these challenges, we present ContrastNER, a prompt-based NER framework that employs both discrete and continuous tokens in prompts and uses a contrastive learning approach to learn the continuous prompts and forecast entity types. The experimental results demonstrate that ContrastNER obtains competitive performance to the state-of-the-art NER methods in high-resource settings and outperforms the state-of-the-art models in low-resource circumstances without requiring extensive manual prompt engineering and verbalizer design.

This paper describes the DATACLOUDDSL language and the DEF-PIPE tool for describing Big Data pipelines. DAT-ACLOUDDSL has both a textual and a visual form and supports requirements obtained both from analyzing existing data pipeline specification tools and from interviews with relevant industrial actors. Particularly, DATACLOUDDSL supports (i) separation of concerns between design and run-time issues, (ii) reuse of previously developed pipeline steps and pipelines in designing new pipelines, (iii) flexible data transfer between pipelines steps and containerization of pipelines and pipeline steps, and (iv) integration of description and simulation components in Big Data pipeline orchestration systems. Additionally, it provides an interface to the discovery and deployment tools of the DataCloud toolbox.

Domain-Specific Languages (DSL) are becoming popular in various fields as they enable domain experts to focus on domain-specific concepts rather than software-specific ones. Many domain experts usually reuse their previously-written scripts for writing new ones; however, to make this process straightforward, there is a need for techniques that can enable domain experts to find existing relevant scripts easily. One fundamental component of such a technique is a model for identifying similar DSL scripts. Nevertheless, the inherent nature of DSLs and lack of data makes building such a model challenging. Hence, in this work, we propose TRANSQL, a transformer-based model for classifying DSL scripts based on their similarities, considering their few-shot context. We build TRANSQL using BERT and GPT-3, two performant language models. Our experiments focus on SQL as one of the most commonly-used DSLs. The experiment results reveal that the BERT-based TRANSQL cannot perform well for DSLs since they need extensive data for the fine-tuning phase. However, the GPT-based TRANSQL gives markedly better and more promising results.

Dangerous events such as accidental falls, allergic reactions or even severe panic attacks can occur spontaneously and within seconds. People experiencing alarming situations like these often require assistance. Modern wearable devices such as smartphones or smartwatches can be used to detect these situations by utilising the plethora of sensors built into them. Mobile Crowdsensing Systems (MCS) might be used to manage the detected alarming situations. To handle these events, an MCS requires integration with mobile sensory devices, as well as the voluntary participation of people willing to help. The contributions of this paper are twofold. First, we enhance the capabilities of an MCS by enabling the integration of various Bluetooth wearable devices. Second, we perform a simulation that models different scenarios that represent dangerous events. Through the MCS simulation, we identify essential parameters that need to be considered when building such a system. The simulation can also be used to find the optimal configuration of the MCS.

Object discovery refers to recognising all unknown objects in images, which is crucial for robotic systems to explore the unseen environment. Recently, object detection models based on deep learning have shown remarkable achievements in object classification and localisation. However, these models have difficulties handling the unseen environment because it is infeasible to exhaustively predefine all types of objects. In this paper, we propose the model YOLOv4-object to recognise all objects in images by modifying the output space of YOLOv4 and related image labels. Experiments on COCO dataset demonstrate the effectiveness of our method by achieving 67.97% recall (6.49% higher than vanilla YOLOv4). We point out that the incomplete labels (COCO only labels for 80 categories) hurt the learning process of object discovery and a higher recall can be achieved by our method if the dataset is fully labelled. Moreover, our approach is transferable, extensible, and compressible, showing broad application scenarios. Finally, we conduct extensive experiments to illustrate the factors that affect the object discovery performance of our model and some suggestions on practical implementations are elaborated.

Organisations possess and continuously generate huge amounts of static and stream data, especially with the proliferation of Internet of Things technologies. Collected but unused data, i.e., Dark Data, mean loss in value creation potential. In this respect, the concept of Computing Continuum extends the traditional more centralised Cloud Computing paradigm with Fog and Edge Computing in order to ensure low latency pre-processing and filtering close to the data sources. However, there are still major challenges to be addressed, in particular related to management of various phases of Big Data processing on the Computing Continuum. In this paper, we set forth an ecosystem for Big Data pipelines in the Computing Continuum and introduce five relevant real-life example use cases in the context of the proposed ecosystem.

The development of the Edge computing paradigm shifts data processing from centralised infrastructures to heterogeneous and geographically distributed infrastructure. Such a paradigm requires data processing solutions that consider data locality in order to reduce the performance penalties from data transfers between remote (in network terms) data centres. However, existing Big Data processing solutions have limited support for handling data locality and are inefficient in processing small and frequent events specific to Edge environments. This paper proposes a novel architecture and a proof-of-concept implementation for software container-centric Big Data workflow orchestration that puts data locality at the forefront. Our solution considers any available data locality information by default, leverages long-lived containers to execute workflow steps, and handles the interaction with different data sources through containers. We compare our system with Argo workflow and show significant performance improvements in terms of speed of execution for processing units of data using our data locality aware Big Data workflow approach.

Big Data processing involves handling large and complex data sets, incorporating different tools and frameworks as well as other processes that help organisations make sense of their data collected from various sources. This set of operations, referred to as Big Data workflows, require taking advantage of the elasticity of cloud infrastructures for scalability. In this paper, we present the design and prototype implementation of a Big Data workflow approach based on the use of software container technologies and message-oriented middleware (MOM) to enable highly scalable workflow execution. The approach is demonstrated in a use case together with a set of experiments that demonstrate the practical applicability of the proposed approach for the scalable execution of Big Data workflows. Furthermore, we present a scalability comparison of our proposed approach with that of Argo Workflows - one of the most prominent tools in the area of Big Data workflows.

With the advent of social media, our online feeds increasingly consist of short, informal, and unstructured text. Instagram is one of the largest social media platforms, containing both text and images. However, most of the prior research on text processing in social media is focused on analyzing Twitter data, and little attention has been paid to text mining of Instagram data. Moreover, many text mining methods rely on training data annotated manually by humans, which in practice is both difficult and expensive to obtain. In this paper, we present methods for weakly supervised text classification of Instagram text. We analyze a corpora of Instagram posts from the fashion domain and train a deep clothing classifier with weak supervision to classify Instagram posts based on the associated text. With our experiments, we demonstrate that in absence of annotated training data, using weak supervision to train models is a viable approach. With weak supervision we were able to label a large dataset in hours, something that would have taken months to do with human annotators. Using the dataset labeled with weak supervision in combination with generative modeling, an [Formula: see text] score of 0.61 is achieved on the task of classifying the image contents of Instagram posts based solely on the associated text, which is on level with human performance.

With around 2.77 billion users using online social media platforms nowadays, it is becoming more attractive for business retailers to reach and to connect to more potential clients through social media. However, providing more effective recommendations to grab clients' attention requires a deep understanding of users' interests. Given the enormous amounts of text and images that users share in social media, deep learning approaches play a major role in performing semantic analysis of text and images. Moreover, object localisation and pixel-bypixel semantic segmentation image analysis neural architectures provide an enhanced level of information. However, to train such architectures in an end-to-end manner, detailed datasets with specific meta-data are required. In our paper, we present a complete framework that can be used to tag images in a hierarchical fashion, and to perform object localisation and semantic segmentation. In addition to this, we show the value of using neural word embeddings in providing additional semantic details to annotators to guide them in annotating images in the system. Our framework is designed to be a fully functional solution capable of providing fine-grained annotations, essential localisation and segmentation services while keeping the core architecture simple and extensible. We also provide a fine-grained labelled fashion dataset that can be a rich source for research purposes.

Data analysis projects typically use different programming languages (from Python for prototyping to C++ for support of runtime constraints) at their different stages by different experts. This creates a need for a data processing framework that is re-usable across multiple programming languages and supports collaboration of experts. In this work, we discuss implementation of a framework which uses a Domain Specific Language (DSL), called WorkflowDSL, that enables domain experts to collaborate on fine-tuning workflows. The framework includes support for parallel execution without any specialized code. It also provides a provenance capturing framework that enables users to analyse past executions and retrieve complete lineage of any data item generated. Graph database is used for storing provenance data. Advantages of usage of a graph database compare to relational databases are demonstrated. Experiments which were performed using a real-world scientific workflow from the bioinformatics domain and industrial data analysis models show that users were able to execute workflows efficiently when using WorkflowDSL for workflow composition and Python for task implementations. Moreover, we show that capturing provenance data can be useful for analysing past workflow executions.

Meeting user demand is one of the most challenging problems arising in public bicycle sharing systems. Various factors, such as daily commuting patterns or topographical conditions, can lead to an unbalanced state where the numbers of rented and returned bicycles differ significantly among the stations. This can cause spatial imbalance of the bicycle inventory which becomes critical when stations run completely empty or full, and thus prevent users from renting or returning bicycles. To prevent such service disruptions, we propose to forecast user demand in terms of expected number of bicycle rentals and returns and accordingly to estimate number of bicycles that need to be manually redistributed among the stations by maintenance vehicles. As opposed to traditional solutions to this problem, which rely on short-term demand forecasts, we aim to maximise the time within which the stations remain balanced by forecasting user demand multiple steps ahead of time. We propose a multi-input multi-output deep learning model based on Long Short-Term Memory networks to forecast user demand over long future horizons. Conducted experimental study over real-world dataset confirms the efficiency and accuracy of our approach.

With the advent of social media, our online feeds increasingly consist of short, informal, and unstructured text. This textual data can be analyzed for the purpose of improving user recommendations and detecting trends. Instagram is one of the largest social media platforms, containing both text and images. However, most of the prior research on text processing in social media is focused on analyzing Twitter data, and little attention has been paid to text mining of Instagram data. Moreover, many text mining methods rely on annotated training data, which in practice is both difficult and expensive to obtain. In this paper, we present methods for unsupervised mining of fashion attributes from Instagram text, which can enable a new kind of user recommendation in the fashion domain. In this context, we analyze a corpora of Instagram posts from the fashion domain, introduce a system for extracting fashion attributes from Instagram, and train a deep clothing classifier with weak supervision to classify Instagram posts based on the associated text. With our experiments, we confirm that word embeddings are a useful asset for information extraction. Experimental results show that information extraction using word embeddings outperforms a baseline that uses Levenshtein distance. The results also show the benefit of combining weak supervision signals using generative models instead of majority voting. Using weak supervision and generative modeling, an F1score of 0.61 is achieved on the task of classifying the image contents of Instagram posts based solely on the associated text, which is on level with human performance. Finally, our empirical study provides one of the few available studies on Instagram text and shows that the text is noisy, that the text distribution exhibits the long-tail phenomenon, and that comment sections on Instagram are multi-lingual.

Selecting the most interesting tourist attractions and planning optimal sightseeing tours can be a difficult task for individuals visiting unfamiliar tourist destinations. On the other hand, the massive amounts of tourists in big cities can collapse certain areas causing transport inefficiency, unbalanced economic growth and nuisance among tourists and citizens. Therefore, the tourist trip planning problem should take into account the possibility for the city government to manage the urban environment and achieve a balanced and sustainable growth. In this paper we introduce the tourist trip planning problem which covers both individual (tourist) and global (city) needs. The planning problem is modelled as an extension of the mixed orienteering problem and can be controlled by deployment of mobility policies which put restrictions on points of interest and routes between them. We propose an algorithmic approach and a software tool to solve this hard combinatorial optimisation problem using variable neighbourhood search. The performance of the proposed algorithm and the tool is assessed over a real-life dataset related to the city of Barcelona. Computational results confirm the efficiency of the algorithm and ability to help both individuals in planning their trips and city governments in achieving sustainable mobility objectives.

Among different components of urban mobility, urban freight transport is usually considered as the least sustainable. Limited traffic infrastructures and increasing demands in dense urban regions lead to frequent delivery runs with smaller freight vehicles. This increases the traffic in urban areas and has negative impacts upon the quality of life in urban populations. Data driven optimizations are essential to better utilize existing urban transport infrastructures and to reduce the negative effects of freight deliveries for the cities. However, there is limited work and data driven research on urban delivery areas and freight transportation networks. In this paper, we collect and analyse data on urban freight deliveries and parking areas towards an optimized urban freight transportation system. Using a new check-in based mobile parking system for freight vehicles, we aim to understand and optimize freight distribution processes. We explore the relationship between areas' availability patterns and underlying traffic behaviour in order to understand the trends in urban freight transport. By applying the detected patterns we predict the availabilities of loading/unloading areas, and thus open up new possibilities for delivery route planning and better managing of freight transport infrastructures.

Online Social Networks (OSNs) remain the focal point of Internet usage. Since the beginning, networking sites tried best to have right privacy mechanisms in place for users, enabling them to share the right content with the right audience. With all these efforts, privacy customizations remain hard for users across the sites. Existing research that address this problem mainly focus on semi-supervised strategies that introduce extra complexity by requiring the user to manually specify initial privacy preferences for their friends. In this work, we suggest an adaptive solution that can dynamically generate privacy labels for users in OSNs. To this end, we introduce a deep reinforcement learning framework that targets two key problems in OSNs like Facebook: the exposure of users' interactions through the network to less trusted direct friends, and the possibility of propagating user updates through direct friends' interactions to indirect friends. By implementing this framework, we aim at understanding how social trust and privacy could be correlated, specifically in a dynamic fashion. We report the ranked dependence between the generated privacy labels and the estimated user trust values, which indicate the ability of the framework to identify the highly trusted users and share with them higher percentages of data.

The question that whether Twitter data can be leveraged to forecast outcome of the elections has always been of great anticipation in the research community. Existing research focuses on leveraging content analysis for positivity or negativity analysis of the sentiments of opinions expressed. This is while, analysis of link structure features of social networks underlying the conversation involving politicians has been less looked. The intuition behind such study comes from the fact that density of conversations about parties along with their respective members, whether explicit or implicit, should reflect on their popularity. On the other hand, dynamism of interactions, can capture the inherent shift in popularity of accounts of politicians. Within this manuscript we present evidence of how a well-known link prediction algorithm, can reveal an authoritative structural link formation within which the popularity of the political accounts along with their neighbourhoods, shows strong correlation with the standing of electoral outcomes. As an evidence, the public time-lines of two electoral events from 2014 elections of Sweden on Twitter have been studied. By distinguishing between member and official party accounts, we report that even using a focus-crawled public dataset, structural link popularities bear strong statistical similarities with vote outcomes. In addition we report strong ranked dependence between standings of selected politicians and general election outcome, as well as for official party accounts and European election outcome.

Current participatory sensing approaches usually do not consider device carriers as intelligent participants in sensing processes. However, modern mobile communication devices allow users express their opinions and judgements which can complement to captured sensor data. In this paper we bring together different modes of mobile crowd sourcing into a general sensing platform which treats device carriers as intelligent problem solvers. We propose a conceptual architecture for versatile context-aware mobile crowd sourcing, and address issues related to data representation, quality control, trust and reputation management, and task allocation. To prove the potential advantages of the proposed conceptual architecture we developed MobiCS, a prototype platform which allows crowd sourcers formulate and distribute both sensing and human intelligence tasks to Android-powered mobile communication devices.

The increasing presence and adoption of Web services on the Web has promoted the significance of management of new service development for service developing sectors. The major challenge is that how to find missing but potentially valuable Web services to be developed. This problem can be divided into two sub-problems: finding missing Web services and measuring the added-value of the introduced services. This paper addresses a plausible solution to the first sub problem. Given a collection of Web services, we propose a framework for suggesting a set of candidate Web services that can be introduced to the collection. These suggested services are novel and do not present in the given collection. Our solution relies on the network structure of Web services for finding and recommending new Web services and utilizes the already observed properties of Web services networks for collective evaluation of the suggested services. The proposed solution is evaluated using 753 semantically annotated Web services. The experimental results shows that the proposed framework provides web service community with new network driven methods for finding and evaluation of new Web services.

In the past years various methods have been developed which require semantic annotations of Web services as an input. Such methods typically leverage discovery, match-making, composition and execution of Web services in dynamic settings. At the same

While the focus of trust research has been mainly on defining and modeling various notions of social trust, less attention has been given to modeling opinion trust. When speaking of social trust mainly homophily (similarity) has been the most successful metric for learning trustworthy links, specially in social web applications such as collaborative filtering recommendation systems. While pure homophily such as Pearson coefficient correlation and its variations, have been favorable to finding taste distances between individuals based on their rated items, they are not necessarily useful in finding opinion distances between individuals discussing a trending topic, e.g. Arab spring. At the same time text mining techniques, such as vector-based techniques, are not capable of capturing important factors such as saliency or polarity which are possible with topical models for detecting, analyzing and suggesting aspects of people mentioning those tags or topics. Thus, in this paper we are proposing to model opinion distances using probabilistic information divergence as a metric for measuring the distances between people's opinion contributing to a discussion in a social network. To acquire feature sets from topics discussed in a discussion we use a very successful topic modeling technique, namely Latent Dirichlet Allocation (LDA). We use the distributions resulting to model topics for generating social networks of group and individual users. Using a Twitter dataset we show that learned graphs exhibit properties of real-world like networks.

Information diffusion has been studied between and within biosphere, microblogs, social networks, citation networks and other domains, where the network structure is present. These studies have turned to be useful for acquiring intrinsic knowledge for strategic decision making in related areas, for example, planning online campaigns in case of microblogs and blogosphere. In the context of data-centric Web services, information exchange patterns will reveal practical heuristics for efficient Web services selection and composition. For example, by possible knowledge that there is information flow between Web services of \textit{Financial Analysis Services} and \textit{Enterprise Resource Planning Services}, as outlined by our experimental results, the potential applications, which interact with \textit{Financial Analysis} services, can be adjusted to take advantage of \textit{Enterprise Resource Planning} services as well. In this paper we present a method for analyzing information diffusion between categories of data-centric Web services. The method operates on a Web services network constructed by linking interface descriptions of categorized Web services. The proposed method is evaluated on a case study of global Web services. The results indicate high potential of the proposed model in understanding interactions between categories of Web services.

In recent years many methods have been proposed, which require semantic annotations of Web services as an input. Such methods include discovery, match-making, composition and execution of Web services in dynamic settings, just to mention few. At the same time automated Web service annotation approaches have been proposed for supporting application of former methods in settings where it is not feasible to provide the annotations manually. However, lack of effective automated evaluation frameworks has seriously limited proper evaluation of the constructed annotations in practical settings where the overall annotation quality of millions of Web services needs to be evaluated. This paper describes an evaluation framework for measuring the quality of semantic annotations of large number of Web services descriptions provided in form of WSDL and XSD documents. The evaluation framework is based on analyzing network properties, namely scale-free and small-world properties, of Web service networks, which in turn have been constructed from semantic annotations of Web services. The evaluation approach is demonstrated through evaluation of a semi-automated annotation approach, which was applied to a set of publicly available WSDL documents describing altogether ca 200 000 Web service operations.

Within this paper we introduce a framework for semi- to full-automatic discovery and acquisition of bag-of-words style interest profiles from openly accessible Social Web communities. To do such, we construct a semantic taxonomy search tree from target domain (domain towards which we're acquiring profiles for), starting with generic concepts at root down to specific-level instances at leaves, then we utilize one of proposed Quest methods, namely Depth-based, N-Split and Greedy to read the concept labels from the tree and crawl the source Social Network for profiles containing corresponding topics. Cached profiles are then mined in a two-step approach, using a clusterer and a classifier to generate predictive model presenting weighted profiles, which are used later on by a semantic recommender to suggest and recommend the community members with the items of their similar interest.

The advent of mobile computing devices and development of wireless and ad-hoc networking technologies has led to growth of infrastructure-less environments. Mostly, these environments lie at the edges of Internet i.e. they are disconnected/sparsely connected to rest of the world. In order to exploit the access to such edges of Internet, we propose and experimentally evaluate an interoperability middleware that synergizes P2P technology, message queuing support and a passive distributed UDDI for Web services discovery and invocation.

In this paper we demonstrate how the Web services based solutions can be effectively utilized and integrated into robotic world. In particular, we consider robotic systems where overall control is not embedded into any of the robots and the local behavior of each robot is loosely dependent on behavior of other robots. We propose an architecture for swarm action planning based on Web services paradigm exploiting a problem ontology for service discovery, linear logic based service composition for action planning and a task allocation layer for finding the most suitable robot to perform an action. In our solution all entities in the system expose their functionalities as Web services and allow dynamic service discovery and selection.

Experiments with large service models of a federated governmental information system are described. Large syntactic Web service models are being used for automatic composition of services in an e-government information system. A visual tool developed in software environment CoCoViLa has been used for handling syntactic service models and synthesis of compound services. For a given specification and a goal, the tool synthesizes a program that generates a service description in OWLS and BPEL.

The deregulation of the electricity industry in many countries has created a number of marketplaces in which producers and consumers can operate in order to more effectively manage and meet their energy needs. To this end, this paper develops a new m

In this paper we propose a compositional logical semantics for business process languages to be used in automatic Web service composition. We introduce a concept of higher order work flow (HOWF) and use it for expressing control of business process. A precise semantics of HOWF enables us both to dynamically generate HOWF for automatic composition of services and to reason about the reachability of goals in process models when HOWF are described manually. Our semantics is general enough to cover different process languages; however, we mainly show its applicability in the context of OWLS and BPEL.

This paper introduces a method for automatic composition of semantic Web services using Linear Logic (LL) theorem proving. The method uses semantic Web service language (DAML-S) for external presentation of Web services, while, internally, the services are presented by extralogical axioms and proofs in LL. We use a process calculus to present the composite service formally. The process calculus is attached to the LL inference rules in the style of type theory. Thus the process model for a composite service can be generated directly from the proof. The subtyping rules that are used for semantic reasoning are presented with LL inference figures. We propose a system architecture where the DAML-S translator, the LL theorem prover and the semantic reasoner can operate together to fulfill the task. This architecture has been implemented in Java.

The overall issue is to improve semantic interoperability among and across agent systems. We propose to use explanation as a way to approach that aim. The explanation process is expressed in terms of an explanation ontology shared by the agents who participate to the explanation session. The explanation ontology is defined in a way general enough to support a variety of explanation mechanisms. We describe the explanation ontology and provide a working through example illustrating how the proposed generic ontology can be used to develop specific explanation mechanism. Finally, the ontology is being integrated into a running agent platform - Agora to demonstrate the practical usefulness of the approach.

With the increasing number of e-commerce services for mobile devices, there are challenges in making these services more personalized and to take into account the severely constrained bandwidth and restricted user interface these devices currently provide. In this paper we consider an agent-based platform for support of mobile commerce using wireless (WAP-based) devices. Agents represent mobile device customers in the network by implementing highly personalized customer profiles. The platform allows customization and adaptation of mobile commerce services as well as pro-active processing and notification of important events. Information to the customers is delivered both via WML-decks and SMS messages. Usage of the platform is illustrated by examples of valued customer membership services and subscription services support. Some details of a prototype platform implementation are briefly considered.

Strategies of the structural synthesis of programs (SSP) of a deductive program synthesis method which is suited for compositional programming in large and is in practical use in a number of programming environments are outlined. SSP is based on a decidable logical calculus where complexity of the proof search is still PSPACE. This requires paying special attention to efficiency of search. Besides the general case of SSP, the authors present synthesis with independent subtasks, synthesis of iterations on regular data structures in terms of SSP and a number of heuristics used for speeding up the search.

The notion of partial deduction known from logic programming is defined in the framework of Structural Synthesis of Programs (SSP). Partial deduction for computability statements in SSP is defined. Completeness and correctness of partial deduction in the framework of SSP are proven. Several tactics and stopping criteria are suggested.