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Information extraction (IE) is the task of automatically extracting structured information from unstructured/semi-structured machine-readable documents. Among various IE tasks, extracting actionable intelligence from an ever-increasing amount of data depends critically upon cross-document coreference resolution (CDCR) - the task of identifying entity mentions across information sources that refer to the same underlying entity. CDCR is the basis of knowledge acquisition and is at the heart of Web search, recommendations, and analytics. Real time processing of CDCR processes is very important and have various applications in discovering must-know information in real-time for clients in finance, public sector, news, and crisis management. Being an emerging area of research and practice, the reported literature on CDCR challenges and solutions is growing fast but is scattered due to the large space, various applications, and large datasets of the order of peta-/tera-bytes. In order to fill this gap, we provide a systematic review of the state of the art of challenges and solutions for a CDCR process. We identify a set of quality attributes, that have been frequently reported in the context of CDCR processes, to be used as a guide to identify important and outstanding issues for further investigations. Finally, we assess existing tools and techniques for CDCR subtasks and provide guidance on selection of tools and algorithms.

Business intelligence applications support decision makers by providing meaningful information from extracted data mainly coming from operational databases and structured data sources. However, the volume of unstructured data is growing very fast especially when analysing external data such as customers’ reviews in social media. It is essential to determine the reputation of the source to the analysts, so that they can take into account the trust value of each source in their analysis. Another important consideration is the semantics of extracted textual data from which meaningful information is derived. The aim of this paper is to provide readers with an understanding of the central concepts and the current state-of-the-art in social trust and semantic analysis of big social data. We provide an in depth analysis of existing challenges and identify set of quality attributes to be used as guide for designing and evaluating architectures of big social trust.

Graph is a fundamental data structure that captures relationships between different data entities. In practice, graphs are widely used for modeling complicated data in different application domains such as social networks, protein networks, transportation networks, bibliographical networks, knowledge bases and many more. Currently, graphs with millions and billions of nodes and edges have become very common. In principle, graph analytics is an important big data discovery technique. Therefore, with the increasing abundance of large graphs, designing scalable systems for processing and analyzing large scale graphs has become one of the most timely problems facing the big data research community. In general, scalable processing of big graphs is a challenging task due to their size and the inherent irregular structure of graph computations. Thus, in recent years, we have witnessed an unprecedented interest in building big graph processing systems that attempted to tackle these challenges. In this article, we provide a comprehensive survey over the state-of-the-art of large scale graph processing platforms. In addition, we present an extensive experimental study of five popular systems in this domain, namely, GraphChi, Apache Giraph, GPS, GraphLab and GraphX. In particular, we report and analyze the performance characteristics of these systems using five common graph processing algorithms and seven large graph datasets. Finally, we identify a set of the current open research challenges and discuss some promising directions for future research in the domain of large scale graph processing.

The Resource Description Framework (RDF) and SPARQL query language are gaining wide popularity and acceptance. In this paper, we present DREAM, a distributed and adaptive RDF system. As opposed to existing RDF systems, DREAM avoids partitioning RDF datasets and partitions only SPARQL queries. By not partitioning datasets, DREAM offers a general paradigm for different types of pattern matching queries, and entirely averts intermediate data shuffling (only auxiliary data are shuffled). Besides, by partitioning queries, DREAM presents an adaptive scheme, which automatically runs queries on various numbers of machines depending on their complexities. Hence, in essence DREAM combines the advantages of the state-of-the-art centralized and distributed RDF systems, whereby data communication is avoided and cluster resources are aggregated. Likewise, it precludes their disadvantages, wherein system resources are limited and communication overhead is typically hindering. DREAM achieves all its goals via employing a novel graph-based, rule-oriented query planner and a new cost model. We implemented DREAM and conducted comprehensive experiments on a private cluster and on the Amazon EC2 platform. Results show that DREAM can significantly outperform three related popular RDF systems.

In today's knowledge-, service-, and cloud-based economy, businesses accumulate massive amounts of data from a variety of sources. In order to understand businesses one may need to perform considerable analytics over large hybrid collections of heterogeneous and partially unstructured data that is captured related to the process execution. This data, usually modeled as graphs, increasingly come to show all the typical properties of big data: wide physical distribution, diversity of formats, non-standard data models, independently-managed and heterogeneous semantics. We use the term big process graph to refer to such large hybrid collections of heterogeneous and partially unstructured process related execution data. Online analytical processing (OLAP) of big process graph is challenging as the extension of existing OLAP techniques to analysis of graphs is not straightforward. Moreover, process data analysis methods 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. While traditional analytics solutions (relational DBs, data warehouses and OLAP), do a great job in collecting data and providing answers on known questions, key business insights remain hidden in the interactions among objects: it will be hard to discover concept hierarchies for entities based on both data objects and their interactions in process graphs. In this paper, we introduce a framework and a set of methods to support scalable graph-based OLAP analytics over process execution data. The goal is to facilitate the analytics over big process graph through summarizing the process graph and providing multiple views at different granularity. To achieve this goal, we present a model for process OLAP (P-OLAP) and define OLAP specific abstractions in process context such as process cubes, dimensions, and cells. We present a MapReduce-based graph processing engine, to support big data analytics over process graphs. We have implemented the P-OLAP framework and integrated it into our existing process data analytics platform, ProcessAtlas, which introduces a scalable architecture for querying, exploration and analysis of large process data. We report on experiments performed on both synthetic and real-world datasets that show the viability and efficiency of the approach.

Social rating systems are subject to unfair evaluations. Users may try to individually or collaboratively promote or demote a product. Detecting unfair evaluations, mainly massive collusive attacks as well as honest looking intelligent attacks, is still a real challenge for collusion detection systems. In this paper, we study the impact of unfair evaluations in online rating systems. First, we study the individual unfair evaluations and their impact on the reputation of people calculated by social rating systems. We then propose a method for detecting collaborative unfair evaluations, also known as collusion. The proposed model uses frequent itemset mining technique to detect the candidate collusion groups and sub-groups. We use several indicators to identify collusion groups and to estimate how destructive such colluding groups can be. The approaches presented in this paper have been implemented in prototype tools, and experimentally validated on synthetic and real-world datasets.

Big data analytics is firmly recognized as a strategic priority for modern enterprises. At the heart of big data analytics lies the data curation process, consists of tasks that transform raw data (unstructured, semi-structured and structured data sources) into curated data, i.e. contextualized data and knowledge that is maintained and made available for use by end-users and applications. To achieve this, the data curation process may involve techniques and algorithms for extracting, classifying, linking, merging, enriching, sampling, and the summarization of data and knowledge. To facilitate the data curation process and enhance the productivity of researchers and developers, we identify and implement a set of basic data curation APIs and make them available as services to researchers and developers to assist them in transforming their raw data into curated data. The curation APIs enable developers to easily add features - such as extracting keyword, part of speech, and named entities such as Persons, Locations, Organizations, Companies, Products, Diseases, Drugs, etc.; providing synonyms and stems for extracted information items leveraging lexical knowledge bases for the English language such as WordNet; linking extracted entities to external knowledge bases such as Google Knowledge Graph and Wikidata; discovering similarity among the extracted information items, such as calculating similarity between string and numbers; classifying, sorting and categorizing data into various types, forms or any other distinct class; and indexing structured and unstructured data - into their data applications. These services can be accessed via a REST API, and the data is returned as a JSON file that can be integrated into data applications. The curation APIs are available as an open source project on GitHub.

This chapter presents process matching techniques that are useful in a series of process model analytics tasks. After presenting some application scenarios such as behavior-based Web service discovery, scientific workflow discovery, and process similarity search, the chapter presents techniques that compare process models’ different perspectives: interface, business protocol, and process model. Finally, the chapter provides an analysis of existing techniques along with challenges and open problems.

This chapter looks at business process data analysis and provides an overview of different aspects of business data analysis techniques and approaches from process/dataspaces to data provenance and data-based querying techniques. After providing an overview of warehousing process data, data services, and dataspaces, the chapter discusses the importance of supporting big data analytics over process execution data. It presents a holistic view of the process executions over various information systems and services (i.e., process space) followed by a brief overview of process mining to highlight the interpretation of the information in the enterprise in the context of process mining. Finally, the chapter focuses on process artifacts and introduces crosscutting aspects in processes data such as time and provenance.

This chapter provides an overview of open-source and commercial software for process analytics. The software for process analytics can be applied to the rich source of events that document the execution of processes and activities within BPM systems, in order to support decision-making in organizations. After providing a summary and comparison of existing open-source and commercial software for process analytics, including real-world use case scenarios, the chapter starts a discussion and future directions on some of the emerging and hot trends in the business process management area such as process spaces, big data for processes, crowdsourcing, social BPM, and process management on the cloud.

This chapter looks at the business process querying techniques and languages and provides an overview of the various techniques for querying business processes. After discussing different techniques for querying the business process execution logs and their related artifacts, the chapter provides an overview of different approaches for utilizing business process querying techniques for ensuring business process compliance to their specifications and business rules.

The big data problem can be seen as a massive number of data islands, ranging from personal, shared, social to business data. The data in these islands is getting large scale, never ending, and ever changing, arriving in batches at irregular time intervals. Examples of these are social and business data. Linking and analyzing of this potentially connected data is of high and valuable interest. In this context, it will be important to investigate how the Linked Data approach can enable the Big Data optimization. In particular, the Linked Data approach has recently facilitated the accessibility, sharing, and enrichment of data on the Web. Scientists believe that Linked Data reduces Big Data variability by some of the scientifically less interesting dimensions. In particular, by applying the Linked Data techniques for exposing structured data and eventually interlinking them to useful knowledge on the Web, many syntactic issues vanish. Generally speaking, this approach improves data optimization by providing some solutions for intelligent and automatic linking among datasets. In this chapter, we aim to discuss the advantages of applying the Linked Data approach, towards the optimization of Big Data in the Linked Open Data (LOD) cloud by: (i) describing the impact of linking Big Data to LOD cloud; (ii) representing various interlinking tools for linking Big Data; and (iii) providing a practical case study: linking a very large dataset to DBpedia.

This chapter provides an overview of the technological landscape surrounding business process management and sets the stage for understanding the different aspects of analyzing business processes with the aim of improving them. The goal of this chapter is to develop an advanced recognition of the potential gaps and thereby an appreciation for key areas of improvement needed to target successful future growth in process analytics. After presenting an overview of the quintessential facets/dimensions often used to describe process types, the chapter examines the various identified implementation technologies and surveys the relevant support tools categorized according to process paradigm.

Online Social Networks (OSNs) are a fertile medium through which users can unleash their opinions and share their thoughts, activities and knowledge of various topics and domains. This medium allows legitimate users as well as spammers to publish their content, leveraging the open environment and fewer restrictions associated with OSNs. Hence, it is essential to evaluate users' credibility in various domains and accordingly make judgements about potentially influential users in a particular domain(s). Most of the existing trustworthiness evaluation approaches of users and their posts in OSNs are generic-based approaches. There is a lack of domain-based trustworthiness evaluation mechanisms. In OSNs, discovering users' influence in a specific domain has been motivated by its significance in a broad range of applications such as personalized recommendation systems and expertise retrieval. The aim of this paper is to present a preliminary approach to evaluating domain-based user's trustworthiness in OSNs. We provide a novel distinguishing measurement for users in a set of knowledge domains. Domains are extracted from the user's content using semantic analysis. In order to obtain the level of trustworthiness, a metric incorporating a number of attributes extracted from content analysis and user analysis is consolidated and formulated considering temporal factor. The approach presented in this paper is promising since it provides a fine-grained trustworthiness analysis of users and their domains of interest in the OSNs.

The original version of this article unfortunately contained a mistake in the acknowledgement statement.

The rising popularity of SaaS allows individuals and enterprises to leverage various services (e.g. Dropbox, Github, GDrive and Yammer) for everyday processes. However, these disparate services do not in general communicate with each other, rather used in an ad-hoc manner with little or no customizable process support. This inevitably leads to “shadow processes”, often only informally managed by e-mail or the like. In this paper, we propose a framework to simplify the integration of disparate services and effectively build customized processes. The implementation of the proposed techniques includes an agile services integration platform, called: CaseWalls. We provide a knowledge-based event-bus for unified interactions between disparate services, while allowing process participants to interact and collaborate on relevant cases.

In this demonstration, we present a framework for enriching business processes with tags specialized into social, resource, location, and temporal. Using the framework, business-process engineers and end-users (i.e., executors) provide the tags with the necessary details which are then automatically propagated from one tag to another, when appropriate. At design time phase of a business process, the propagation of relations between tags reflects unidirectional-transfer-of-final-details, unidirectional-transfer-of-partial-details, and bidirectional-transfer-of-partial-details while at run-time the propagation of relations reflects strong-trigger, weak-trigger, and meet-in-the-middle trigger. Our provides an elegant mechanism for monitoring business processes which is more user-driven than traditional approaches which heavily rely on log analysis mechanisms.

Online rating systems are subject to unfair evaluations. Users may try to individually or collaboratively promote or demote a product. Collaborative unfair rating, i.e., collusion, is more damaging than individual unfair rating. Detecting massive collusive attacks as well as honest looking intelligent attacks is still a real challenge for collusion detection systems. In this paper, we study impact of collusion in online rating systems and asses their susceptibility to collusion attacks. The proposed model uses frequent itemset mining technique to detect candidate collusion groups and sub-groups. Then, several indicators are used for identifying collusion groups and to estimate how damaging such colluding groups might be. The model has been implemented and we present results of experimental evaluation of our methodology.

Processes in case management applications are flexible, knowledge-intensive and people-driven, and often used as guides for workers in processing of artifacts. An important fact is the evolution of process artifacts over time as they are touched by different people in the context of a knowledge-intensive process. This highlights the need for tracking process artifacts in order to find out their history (artifact versioning) and also provenance (where they come from, and who touched and did what on them). We present a framework, simple abstractions and a language for analyzing cross-cutting aspects (in particular versioning and provenance) over process artifacts. We introduce two concepts of timed-folders to represent evolution of artifacts over time, and activity-paths to represent the process which led to artifacts. The introduced approaches have been implemented on top of FPSPARQL, Folder-Path enabled extension of SPARQL, and experimentally validated on real-world datasets.

Worker selection is a significant and challenging issue in crowdsourcing systems. Such selection is usually based on an assessment of the reputation of the individual workers participating in such systems. However, assessing the credibility and adequacy of such calculated reputation is a real challenge. In this paper, we propose a reputation management model which leverages the values of the tasks completed, the credibility of the evaluators of the results of the tasks and time of evaluation of the results of these tasks in order to calculate more dependable quality metrics for workers and evaluators. The model has been implemented and experimentally validated.

Graphs are essential modeling and analytical objects for representing information networks. Existing approaches, in on-line analytical processing on graphs, took the first step by supporting multi-level and multi-dimensional queries on graphs, but they do not provide a semantic-driven framework and a language to support n-dimensional computations, which are frequent in OLAP environments. The major challenge here is how to extend decision support on multidimensional networks considering both data objects and the relationships among them. Moreover, one of the critical deficiencies of graph query languages, e.g. SPARQL, is the lack of support for n-dimensional computations. In this paper, we propose a graph data model, GOLAP, for online analytical processing on graphs. This data model enables extending decision support on multidimensional networks considering both data objects and the relationships among them. Moreover, we extend SPARQL to support n-dimensional computations. The approaches presented in this paper have been implemented on top of FPSPARQL, Folder-Path enabled extension of SPARQL, and experimentally validated on synthetic and real-world datasets.

The execution of a business process (BP) in today's enterprises may involve a workflow and multiple IT systems and services. Often no complete, up-to-date documentation of the model or correlation information of process events exist. Understanding the execution of a BP in terms of its scope and details is challenging specially as it is subjective: depends on the perspective of the person looking at BP execution. We present a framework, simple abstractions and a language for the explorative querying and understanding of BP execution from various user perspectives. We propose a query language for analyzing event logs of process-related systems based on the two concepts of folders and paths, which enable an analyst to group related events in the logs or find paths among events. Folders and paths can be stored to be used in follow-on analysis. We have implemented the proposed techniques and the language, FPSPARQL, by extending SPARQL graph query language. We present the evaluation results on the performance and the quality of the results using a number of process event logs.