Generative Pre-trained Transformer (GPT) is a state-of-the-art machine learning model capable of generating human-like text through natural language processing (NLP). GPT is trained on massive amounts of text data and uses deep learning techniques to learn patterns and relationships within the data, enabling it to generate coherent and contextually appropriate text. This position paper proposes using GPT technology to generate new process models when/if needed. We introduce ProcessGPT as a new technology that has the potential to enhance decision-making in data-centric and knowledge-intensive processes. ProcessGPT can be designed by training a generative pre-trained transformer model on a large dataset of business process data. This model can then be fine-tuned on specific process domains and trained to generate process flows and make decisions based on context and user input. The model can be integrated with NLP and machine learning techniques to provide insights and recommendations for process improvement. Furthermore, the model can automate repetitive tasks and improve process efficiency while enabling knowledge workers to communicate analysis findings, support evidence, and make decisions. ProcessGPT can revolutionize business process management (BPM) by offering a powerful tool for process automation and improvement. Finally, we demonstrate how ProcessGPT can be a powerful tool for augmenting data engineers in maintaining data ecosystem processes within large bank organizations. Our scenario highlights the potential of this approach to improve efficiency, reduce costs, and enhance the quality of business operations through the automation of data-centric and knowledge-intensive processes. These results underscore the promise of ProcessGPT as a transformative technology for organizations looking to improve their process workflows.

Removing noise from scanned pages is a vital step before their submission to optical character recognition (OCR) system. Most available image denoising methods are supervised where the pairs of noisy/clean pages are required. However, this assumption is rarely met in real settings. Besides, there is no single model that can remove various noise types from documents. Here, we propose a unified end-to-end unsupervised deep learning model, for the first time, that can effectively remove multiple types of noise, including salt & pepper noise, blurred and/or faded text, as well as watermarks from documents at various levels of intensity. We demonstrate that the proposed model significantly improves the quality of scanned images and the OCR of the pages on several test datasets.

Business documents are central to the operation of all organizations, and they come in all shapes and sizes: project reports, planning documents, technical specifications, financial statements, meeting minutes, legal agreements, contracts, resumes, purchase orders, invoices, and many more. The ability to read, understand and interpret these documents, referred to here as Document Intelligence (DI), is challenging due to not only many domains of knowledge involved, but also their complex formats and structures, internal and external cross references deployed, and even less-than-ideal quality of scans and OCR oftentimes performed on them. This workshop aims to explore and advance the current state of research and practice in answering these challenges.

In the regular course of business, companies spend a lot of effort reading and interpreting documents, a highly manual process that involves tedious tasks, such as identifying dates and names or locating the presence or absence of certain clauses in a contract. Dealing with natural language is complex and further complicated by the fact that these documents come in various formats (scanned image, digital formats) and have different degrees of internal structure (spreadsheets, invoices, text documents). We present DICR, an end-to-end, modular, and trainable system that automates the mundane aspects of document review and allows humans to perform the validation. The system is able to speed up this work while increasing quality of information extracted, consistency, throughput, and decreasing time to decision. Extracted data can be fed into other downstream applications (from dashboards to Q&A and to report generation).

Business process monitoring techniques have been investigated in depth over the last decade to enable organizations to deliver process insight. Recently, a new stream of work in predictive business process monitoring leveraged deep learning techniques to unlock the potential business value locked in process execution event logs. These works use Recurrent Neural Networks, such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), and suffer from misinformation and accuracy as they use the last hidden state (as the context vector) for the purpose of predicting the next event. On the other hand, in operational processes, traces may be very long, which makes the above methods inappropriate for analyzing them. In addition, in predicting the next events in a running case, some of the previous events should be given a higher priority. To address these shortcomings, in this paper, we present a novel approach inspired by the notion of attention mechanism, utilized in Natural Language Processing and, particularly, in Neural Machine Translation. Our proposed approach uses all hidden states to accurately predict future behavior and the outcome of individual activities. Experimental evaluation of real-world event logs revealed that the use of attention mechanisms in the proposed approach leads to a more accurate prediction.

Enabling the analysis of behavioral disorders over time in social networks, can help in suicide prevention, (school) bullying detection and extremist/criminal activity prediction. In this paper, we present a novel data analytics pipeline to enable the analysis of patterns of behavioral disorders on social networks. We present a Social Behavior Graph (sbGraph) model, to enable the analysis of factors that are driving behavior disorders over time. We use the golden standards in personality, behavior and attitude to build a domain specific Knowledge Base (KB). We use this domain knowledge to design cognitive services to automatically contextualize the raw social data and to prepare them for behavioral analytics. Then we introduce a pattern-based word embedding technique, namely personality2vec, on each feature extracted to build the sbGraph. The goal is to use mathematical embedding from a space with a dimension per feature to a continuous vector space which can be mapped to classes of behavioral disorders (such as cyber-bullying and radicalization) in the domain specific KB. We implement an interactive dashboard to enable social network analysts to analyze and understand the patterns of behavioral disorders over time. We focus on a motivating scenario in Australian government's office of the e-Safety commissioner, where the goal is to empowering all citizens to have safer, more positive experiences online.

No abstract available.

Summary In today's knowledge‐, service‐, and cloud‐based economy, an overwhelming amount of business‐related data are being generated at a fast rate daily from a wide range of sources. These data increasingly show all the typical properties of big data: wide physical distribution, diversity of formats, nonstandard data models, and independently managed and heterogeneous semantics. In this context, there is a need for new scalable and process‐aware services for querying, exploration, and analysis of process data in the enterprise because (1) process data analysis services should be capable of processing and querying large amount of data effectively and efficiently and, therefore, have to be able to scale well with the infrastructure's scale and (2) the querying services need to enable users to express their data analysis and querying needs using process‐aware abstractions rather than other lower‐level abstractions. In this paper, we introduce ProcessAtlas, ie, an extensible large‐scale process data querying and analysis platform for analyzing process data in the enterprise. The ProcessAtlas platform offers an extensible architecture by adopting a service‐based model so that new analytical services can be plugged into the platform. In ProcessAtlas, we present a domain‐specific model for representing process knowledge, ie, process‐level entities, abstractions, and the relationships among them modeled as graphs. We provide services for discovering, extracting, and analyzing process data. We provide efficient mapping and execution of process‐level queries into graph‐level queries by using scalable process query services to deal with the process data size growth and with the infrastructure's scale. We have implemented ProcessAtlas as a MapReduce‐based prototype and report on experiments performed on both synthetic and real‐world datasets.

This special issue contains extended versions of the outstanding papers presented at the 13th International Conference on Business Process Management (BPM) that took place in Innsbruck, Austria on August 31–September 3, 2015.

A large volume of Open Data is being generated on a continuous basis. Examples of this are the case of social, natural, and information systems such as World Wide Web and social networks. Most entities and objects in the Open Data are interconnected, forming a complex, semi-structured, and information-rich networks. In this sense, Linked Open Data has the potential to be similar to a federated database. Since Linked Open Data is based on W3C standards, it is possible to implement a federation infrastructure, however, the current SPARQL standard makes it challenging to analyze the Open Data in an explorative manner. Consequently, it will be hard to discover the hidden knowledge in the relationships among entities in Open Data sources. In this paper, we present Galaxy, a platform for explorative analysis of Open Data Sources. Galaxy facilitates the analysis of Open Data graphs based on simple abstractions, i.e. folders and paths, which enable an analyst to group related entities in the graph or nd paths among entities. Galaxy uses Hadoop data processing platforms to store and retrieve large numbers of RDF triples and to support cost-eective and Web-scale processing of Semantic Web data through a Folder-Path enabled extension of SPARQL.

This paper introduces a scalable process event analysis approach, including parallel algorithms, to support efficient event correlation for big process data. It proposes a two-stages approach for finding potential event relationships, and their verification over big event datasets using MapReduce framework. We report on the experimental results, which show the scalability of the proposed methods, and also on the comparative analysis of the approach with traditional non-parallel approaches in terms of time and cost complexity.

In IT strategic outsourcing businesses, it is critical to have competent deal teams design competitive service solutions and swiftly respond to clients' requests for proposals. In this paper we present a general team recommendation framework for finding the best deal teams to pursue such engagement opportunities. Little previous work on team recommendations considers both individual and team-level features at the same time. Our proposed framework can take into account diverse individual and team features, and accommodate various cost or feature functions. We introduce a team quality metric based on a weighted linear combination of these features, the weights of which are learned using a machine learning approach by leveraging historical project outcomes. A combinatorial optimization algorithm is finally applied to search the possible solution space for the approximate best team. We report a preliminary evaluation of our framework by applying it to real-world data from strategic outsourcing businesses at a large IT service company. We also compare our approach with other existing work by using the public DBLP dataset for recommending teams in academic paper authoring.

Social media have become a global phenomenon affecting people in their private lives and in their personal interactions, particularly among younger people. It is thus not surprising that social media are also being explored in professional contexts such as in enterprises, where a number of social media platforms and social extensions to existing workgroup and collaboration systems have been emerging. In this paper, we consider one such platform that was developed for the internal use in a large global enterprise (HP). We present data and analysis of how this social media platform has been used in HP over the past five years. We then present conclusions from this analysis and relate them to work patterns in enterprises with the goal of advancing social media to the next level making them better fit the work context and more relevant for people in their work functions. We consider enterprise sales processes as a case study and present a number of extensions for our social media platform.

Many cloud service providers offer outsourcing capabilities to businesses using the software-as-a-service delivery model. In this delivery model, sensitive business data need to be stored and processed outside the control of the business. The ability to manage data in compliance with regulatory and corporate policies, which we refer to as data assurance, is an essential success factor for this delivery model. There exist challenges to express service data assurance capabilities, capture customers' requirements, and enforce these policies inside service providers' environments. This paper addresses these challenges by proposing Global Enforcement Of Data Assurance Controls (GEODAC), a policy framework that enables the expression of both service providers' capabilities and customers' requirements, and enforcement of the agreed-upon data assurance policies in service providers' environments. High-level policy statements are backed in the service environment with a state machine-based representation of policies in which each state represents a data lifecycle stage. Data assurance policies that define requirements on data retention, data migration, data appropriateness for use, etc. can be described and enforced. The approach has been implemented in a prototype tool and evaluated in a services environment.

There is a push in the enterprise towards facilitating processes from best practice frameworks (such as the IT Infrastructure Library (ITIL)) to make them more repeatable, efficient and cost-effective. Best practice processes provide descriptive, high level guidelines rather than prescriptive, precise process model definitions. They are meant to be followed by people and may be adapted and enacted differently in various realizations. Currently, ITIL processes are either supported by tools that hard code an interpretation of the process logic, or followed by people using productivity tools. This is inefficient because existing tools hardcode a rigid logic of the processes, and do not support collaborative and flexible realizations of processes. Moreover, there is a risk of information loss when people using rigid productivity tools, and are forced to collaborate outside of those tools. In this paper, we present a conversation-centered approach and a tool that enables dynamic and flexible definition and enactment of best practice processes in a collaborative and interactive manner. We address the issue of information loss by using the concept of a conversation as a container of information about the interactions among people in the context of a process. A conversation is backed with a semi-structured process model and process templates to support flexible and adaptive process realization. We showcase the approach using an illustrative use case in incident and problem management, based on best practice processes from ITIL.

With the rapid growth in the number of online Web services, the problem of service adaptation has received significant attention. In matching and adaptation, the functional description of services including interface and data as well as behavioral descriptions are important. Existing work on matching and adaptation focuses only on one aspect.

Standardization in Web services simplifies integration. However, it does not remove the need for adapters due to possible heterogeneity among service interfaces and protocols. In this paper, we characterize the problem of Web services adaptation focusing on business interfaces and protocols adapters. Our study shows that many of the differences between business interfaces and protocols are recurring. We introduce mismatch patterns to capture these recurring differences and to provide solutions to resolve them. We leverage mismatch patterns for service adaptation with two approaches: by developing stand-alone adapters and via service modification. We then dig into the notion of adaptation aspects that, following aspect-oriented programming paradigm and service modification approach, allow for rapid development of adapters. We present a study showing that it is a preferable approach in many cases. The proposed approach is implemented in a proof-of-concept prototype tool, and evaluated using both qualitative and quantitative methods.

Business processes (BPs) are central to the operation of both public and private organizations. A business process is a set of coordinated tasks and activities to achieve a business objective or goal. Given the importance of BPs to overall efficiency and effectiveness, the competitiveness of organizations hinges on continuous BP improvement. In the nineties, the focus of BP improvement was on automation: workflow management systems (WfMSs) and other middleware technologies were used to reduce cost and improve efficiency by providing better system integration and automated enactment of operational business processes. Recently, the focus of business process has expanded to monitoring, analysis and understanding of business processes, and such techniques are incorporated in business process management systems (BPMSs).

Understanding the business (interaction) protocol supported by a service is very important for both clients and service providers: it allows developers to know how to write clients that interact with a service, and it allows development tools and runtime middleware to deliver functionality that simplifies the service development lifecycle. It also greatly facilitates the monitoring, visualization, and aggregation of interaction data. This paper presents an approach for discovering protocol definitions from real-world service interaction logs. It first describes the challenges in protocol discovery in such a context. Then, it presents a novel discovery algorithm, which is widely applicable, robust to different kinds of imperfections often present in realworld service logs, and able to derive protocols of small sizes, also thanks to heuristics. As finding the most precise and the smallest model is algorithmically not feasible from imperfect service logs, finally, the paper presents an approach to refine the discovered protocol via user interaction, to compensate for possible imprecision introduced in the discovered model. The approach has been implemented and experimental results show its viability on both synthetic and real-world datasets.

This paper deals with the problem of discovering protocol models by analyzing real-world interaction logs. There are several scenarios where protocol discovery is useful and needed: (i) In practice, the protocol definition may not be available. This can happen for many reasons, e.g., the service has been developed using a bottom-up approach, by simply SOAP-ifying an existing application; (ii) even when the protocol model is available, protocol discovery is important as we may want to verify if the designed protocol model is what is actually being supported by the implementation and, if not, what are the differences. An instance of this problem involves discovering if the service is compliant with the protocol specification required by some domain-specific standardization body or industry consortium.

In service-oriented computing, a conversation is a sequence of message exchanges between two or more services to achieve a certain goal, for example to order and pay for goods. A business protocol of a service is a specification of the possible conversations that a service can have with its partners. Motivated by the goal of facilitating the scalable development and maintenance of service oriented applications, especially in light of the many benefits of protocols, we have developed ServiceMosaic (servicemosaic.isima.fr), a platform for Web services life-cycle management. ServiceMosaic is an interactive and model-driven CASE tool for managing Web service interactions, which consists of two broad modules: protocol discovery and protocol management.

In today's Web, many functionality-wise similar Web services are offered through heterogeneous interfaces (operation definitions) and business protocols (ordering constraints defined on legal operation invocation sequences). The typical approach to enable interoperation in such a heterogeneous setting is through developing adapters. There have been approaches for classifying possible mismatches between service interfaces and business protocols to facilitate adapter development. However, the hard job is that of identifying, given two service specifications, the actual mismatches between their interfaces and business protocols. In this paper we present novel techniques and a tool that provides semi-automated support for identifying and resolution of mismatches between service interfaces and protocols, and for generating adapter specification. We make the following main contributions: (i) we identify mismatches between service interfaces, which leads to finding mismatches of type of signature, merge/split, and extra/missing messages; (ii) we identify all ordering mismatches between service protocols and generate a tree, called mismatch tree, for mismatches that require developers' input for their resolution. In addition, we provide semi-automated support in analyzing the mismatch tree to help in resolving such mismatches. We have implemented the approach in a tool inside IBM WID (WebSphere Integration Developer). Our experiments with some real-world case studies show the viability of the proposed approach. The methods and tool are significant in that they considerably simplify the problem of adapting services so that interoperation is possible.