Publications
2020 |
Borgida Alexander; Kalokyri, Varvara; Marian Amélie Description Logics and Specialization for Structured BPMN Inproceedings International Conference on Business Process Management, pp. 19–31, 2020. @inproceedings{borgida2019description, title = {Description Logics and Specialization for Structured BPMN}, author = {Borgida, Alexander; Kalokyri, Varvara; Marian, Amélie}, year = {2020}, date = {2020-01-03}, booktitle = {International Conference on Business Process Management}, pages = {19--31}, abstract = {The literature contains arguments for the benefits of representing and reasoning with BPMN processes in (OWL) ontologies, but these proposals are not able to reason about their dynamics. We introduce a new Description Logic, sBPMprocessDL, to represent the behavioral semantics of (block) structured BPMN. It supports reasoning about process concepts based on their execution traces. Starting from the traditional notion of subsumption in Description Logics (including sBPMprocessDL), we further investigate the notions of specialization and inheritance, as a way to help build and abbreviate large libraries of processes in an ontology, which are needed in many applications. We also provide formal evidence for the intuition that features of structured BPMN diagrams such as AND-gates and sub-processes can provide substantial benefits for their succinctness. The same can be true when moving from a structured to an equivalent unstructured version.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } The literature contains arguments for the benefits of representing and reasoning with BPMN processes in (OWL) ontologies, but these proposals are not able to reason about their dynamics. We introduce a new Description Logic, sBPMprocessDL, to represent the behavioral semantics of (block) structured BPMN. It supports reasoning about process concepts based on their execution traces. Starting from the traditional notion of subsumption in Description Logics (including sBPMprocessDL), we further investigate the notions of specialization and inheritance, as a way to help build and abbreviate large libraries of processes in an ontology, which are needed in many applications. We also provide formal evidence for the intuition that features of structured BPMN diagrams such as AND-gates and sub-processes can provide substantial benefits for their succinctness. The same can be true when moving from a structured to an equivalent unstructured version. |
2019 |
Vianna Daniela; Kalokyri, Varvara; Borgida Alexander; Marian Amélie; Nguyen Thu. Searching heterogeneous personal digital traces Inproceedings Proceedings of the 82nd Annual Meeting of Association for Information Science and Technology, ASIS&T 2019, Melbourne, Australia, October 19-23, 2019., pp. 276-285, John Wiley & Sons, Inc., 2019. @inproceedings{vianna2019searching, title = {Searching heterogeneous personal digital traces}, author = {Vianna, Daniela; Kalokyri, Varvara; Borgida, Alexander; Marian, Amélie; Nguyen, Thu.}, doi = {https://doi.org/10.1002/pra2.22}, year = {2019}, date = {2019-10-18}, booktitle = {Proceedings of the 82nd Annual Meeting of Association for Information Science and Technology, ASIS&T 2019, Melbourne, Australia, October 19-23, 2019.}, volume = {56}, pages = {276-285}, publisher = {John Wiley & Sons, Inc.}, abstract = {Digital traces of our lives are now constantly produced by various connected devices, internet services and interactions. Our actions result in a multitude of heterogeneous data objects, or traces, kept in various locations in the cloud or on local devices. Users have very few tools to organize, understand, and search the digital traces they produce. We propose a simple but flexible data model to aggregate, organize, and find personal information within a collection of a user's personal digital traces. Our model uses as basic dimensions the six questions: what, when, where, who, why, and how. These natural questions model universal aspects of a personal data collection and serve as unifying features of each personal data object, regardless of its source. We propose indexing and search techniques to aid users in searching for their past information in their unified personal digital data sets using our model. Experiments performed over real user data from a variety of data sources such as Facebook, Dropbox, and Gmail show that our approach significantly improves search accuracy when compared with traditional search tools.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Digital traces of our lives are now constantly produced by various connected devices, internet services and interactions. Our actions result in a multitude of heterogeneous data objects, or traces, kept in various locations in the cloud or on local devices. Users have very few tools to organize, understand, and search the digital traces they produce. We propose a simple but flexible data model to aggregate, organize, and find personal information within a collection of a user's personal digital traces. Our model uses as basic dimensions the six questions: what, when, where, who, why, and how. These natural questions model universal aspects of a personal data collection and serve as unifying features of each personal data object, regardless of its source. We propose indexing and search techniques to aid users in searching for their past information in their unified personal digital data sets using our model. Experiments performed over real user data from a variety of data sources such as Facebook, Dropbox, and Gmail show that our approach significantly improves search accuracy when compared with traditional search tools. |
2018 |
Kalokyri Varvara; Borgida, Alexander; Marian Amélie YourDigitalSelf: A Personal Digital Trace Integration Tool Inproceedings Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy, October 22-26, 2018, pp. 1963–1966, 2018. @inproceedings{DBLP:conf/cikm/KalokyriBM18, title = {YourDigitalSelf: A Personal Digital Trace Integration Tool}, author = {Kalokyri, Varvara; Borgida, Alexander; Marian, Amélie}, url = {https://doi.org/10.1145/3269206.3269219 https://yourdigitalself.github.io/papers/cikm_Kalokyri.pdf}, doi = {10.1145/3269206.3269219}, year = {2018}, date = {2018-01-01}, booktitle = {Proceedings of the 27th ACM International Conference on Information and Knowledge Management, CIKM 2018, Torino, Italy, October 22-26, 2018}, pages = {1963--1966}, crossref = {DBLP:conf/cikm/2018}, abstract = {Personal information is typically fragmented across multiple, het- erogeneous, distributed sources and saved as small, heterogeneous data objects, or traces. The DigitalSelf project at Rutgers University focuses on developing tools and techniques to manage (organize, search, summarize, make inferences on and personalize) such het- erogeneous collections of personal digital traces. We propose to demonstrate YourDigitalSelf, a mobile phone-based personal infor- mation organization application developed as part of the DigitalSelf project. The demonstration will use a sample user data set to show how several disparate data traces can be integrated and combined to create personal narratives, or coherent episodes, of the user’s activities. Conference attendees will be given the option to install YourDigitalSelf on their own devices to interact with their own data.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Personal information is typically fragmented across multiple, het- erogeneous, distributed sources and saved as small, heterogeneous data objects, or traces. The DigitalSelf project at Rutgers University focuses on developing tools and techniques to manage (organize, search, summarize, make inferences on and personalize) such het- erogeneous collections of personal digital traces. We propose to demonstrate YourDigitalSelf, a mobile phone-based personal infor- mation organization application developed as part of the DigitalSelf project. The demonstration will use a sample user data set to show how several disparate data traces can be integrated and combined to create personal narratives, or coherent episodes, of the user’s activities. Conference attendees will be given the option to install YourDigitalSelf on their own devices to interact with their own data. |
2017 |
Kalokyri, Varvara; Borgida, Alexander; Marian, Amélie; Vianna, Daniela Semantic Modeling and Inference with Episodic Organization for Managing Personal Digital Traces Inproceedings On the Move to Meaningful Internet Systems. OTM 2017 Conferences - Confederated International Conferences: CoopIS, C&TC, and ODBASE 2017, Rhodes, Greece, October 23-27, 2017, Proceedings, Part II, pp. 273–280, 2017. @inproceedings{DBLP:conf/otm/KalokyriBMV17, title = {Semantic Modeling and Inference with Episodic Organization for Managing Personal Digital Traces}, author = {Varvara Kalokyri and Alexander Borgida and Amélie Marian and Daniela Vianna}, url = {https://doi.org/10.1007/978-3-319-69459-7_19 https://yourdigitalself.github.io/papers/odbase_Kalokyri.pdf}, doi = {10.1007/978-3-319-69459-7_19}, year = {2017}, date = {2017-01-01}, booktitle = {On the Move to Meaningful Internet Systems. OTM 2017 Conferences - Confederated International Conferences: CoopIS, C&TC, and ODBASE 2017, Rhodes, Greece, October 23-27, 2017, Proceedings, Part II}, pages = {273--280}, crossref = {DBLP:conf/otm/2017-2}, abstract = {Many individuals generate a flood of personal digital traces (e.g., emails, social media posts, web searches, calendars) as a byproduct of their daily activities. To facilitate querying and to support natural retrospective and prospective memory of these, a key problem is to integrate them in some sensible manner. For this purpose, based on research in the cognitive sciences, we propose a conceptual modeling language whose novel features include i) the super-properties “who, what, when, where, why, how” applied uniformly to both documents and autobiographic events; and ii) the ability to describe prototypical plans (“scripts”) for common everyday events, which in fact generate personal digital documents as traces. The scripts and wh-questions support the hierarchical organization and abstraction of the original data, thus helping end-users query it. We illustrate the use of our language through examples, provide formal semantics, and present an algorithm to recognize script instances.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Many individuals generate a flood of personal digital traces (e.g., emails, social media posts, web searches, calendars) as a byproduct of their daily activities. To facilitate querying and to support natural retrospective and prospective memory of these, a key problem is to integrate them in some sensible manner. For this purpose, based on research in the cognitive sciences, we propose a conceptual modeling language whose novel features include i) the super-properties “who, what, when, where, why, how” applied uniformly to both documents and autobiographic events; and ii) the ability to describe prototypical plans (“scripts”) for common everyday events, which in fact generate personal digital documents as traces. The scripts and wh-questions support the hierarchical organization and abstraction of the original data, thus helping end-users query it. We illustrate the use of our language through examples, provide formal semantics, and present an algorithm to recognize script instances. |
Kalokyri, Varvara; Borgida, Alexander; Marian, Amélie; Vianna, Daniela Integration and Exploration of Connected Personal Digital Traces Inproceedings Proceedings of the ExploreDB'17, Chicago, IL, USA, May 19, 2017, pp. 3:1–3:6, 2017. @inproceedings{DBLP:conf/sigmod/KalokyriBMV17, title = {Integration and Exploration of Connected Personal Digital Traces}, author = {Varvara Kalokyri and Alexander Borgida and Amélie Marian and Daniela Vianna}, url = {https://doi.org/10.1145/3077331.3077337 https://yourdigitalself.github.io/papers/exploreDB_Kalokyri.pdf}, doi = {10.1145/3077331.3077337}, year = {2017}, date = {2017-01-01}, booktitle = {Proceedings of the ExploreDB'17, Chicago, IL, USA, May 19, 2017}, pages = {3:1--3:6}, crossref = {DBLP:conf/sigmod/2017exploredb}, abstract = {A large number of personal digital traces is constantly generated or available online from a variety of sources, such as social me- dia, calendars, purchase history, etc. These personal data traces are fragmented and highly heterogeneous, raising the need for an integrated view of the user’s activities. Prior research in Personal Information Management focused mostly on creating a static model of the world (objects and their relationships). We argue that a dy- namic world view is also helpful for making sense of collections of related personal documents, and propose a partial solution based on scripts – a theoretically well-founded idea in AI and Cognitive Science. Scripts are stereotypical hierarchical plans for everyday activities, involving interactions between mostly social agents. We augment these with hints of the digital traces that they can leave. By connecting Personal Digital Traces through scripts, we can build an episodic view of users’ digital memories, which allow users to explore related events and actions in an integrated way. The paper uses the Eating_Out script for illustration, and ends with a report on the results of a case-study of applying a prototype implementation on real user data.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } A large number of personal digital traces is constantly generated or available online from a variety of sources, such as social me- dia, calendars, purchase history, etc. These personal data traces are fragmented and highly heterogeneous, raising the need for an integrated view of the user’s activities. Prior research in Personal Information Management focused mostly on creating a static model of the world (objects and their relationships). We argue that a dy- namic world view is also helpful for making sense of collections of related personal documents, and propose a partial solution based on scripts – a theoretically well-founded idea in AI and Cognitive Science. Scripts are stereotypical hierarchical plans for everyday activities, involving interactions between mostly social agents. We augment these with hints of the digital traces that they can leave. By connecting Personal Digital Traces through scripts, we can build an episodic view of users’ digital memories, which allow users to explore related events and actions in an integrated way. The paper uses the Eating_Out script for illustration, and ends with a report on the results of a case-study of applying a prototype implementation on real user data. |
2014 |
Vianna, Daniela; Yong, Alicia-Michelle; and Xia, Chaolun; Marian, Amélie; Nguyen, Thu D A tool for personal data extraction Inproceedings Workshops Proceedings of the 30th International Conference on Data Engineering Workshops, ICDE 2014, Chicago, IL, USA, March 31 - April 4, 2014, pp. 80–83, 2014. @inproceedings{DBLP:conf/icde/ViannaYXMN14, title = {A tool for personal data extraction}, author = {Daniela Vianna and Alicia-Michelle Yong and and Chaolun Xia and Amélie Marian and Thu D Nguyen}, url = {https://doi.org/10.1109/ICDEW.2014.6818307 https://yourdigitalself.github.io/papers/icde_Vianna.pdf}, doi = {10.1109/ICDEW.2014.6818307}, year = {2014}, date = {2014-01-01}, booktitle = {Workshops Proceedings of the 30th International Conference on Data Engineering Workshops, ICDE 2014, Chicago, IL, USA, March 31 - April 4, 2014}, pages = {80--83}, crossref = {DBLP:conf/icde/2014w}, abstract = {Digital storage now acts as an archive of the memories of users worldwide, keeping record of data as well as the context in which the data was acquired. The massive amount of data available and the fact that it is fragmented across many services (e.g., Facebook) and devices (e.g., laptop) make it very difficult for users to find specific pieces of information that they remember having stored or accessed. Unifying this fragmented data into a single data set that includes contextual information would allow for much better indexing and searching of personal information. Thus, we have developed a personal data extraction tool as a first step toward this vision. In this paper, we present this extraction tool, along with some preliminary statistics about personal data gathered by the tool for several users. The goal of the data analysis is to give a glimpse of what the digital life of a person may look like, and how it is currently partitioned across many different services; moreover, it reinforces the fact that it is not possible for users to manually retrieve, store and access their extensive digital data without the support of a personalized information management tool.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Digital storage now acts as an archive of the memories of users worldwide, keeping record of data as well as the context in which the data was acquired. The massive amount of data available and the fact that it is fragmented across many services (e.g., Facebook) and devices (e.g., laptop) make it very difficult for users to find specific pieces of information that they remember having stored or accessed. Unifying this fragmented data into a single data set that includes contextual information would allow for much better indexing and searching of personal information. Thus, we have developed a personal data extraction tool as a first step toward this vision. In this paper, we present this extraction tool, along with some preliminary statistics about personal data gathered by the tool for several users. The goal of the data analysis is to give a glimpse of what the digital life of a person may look like, and how it is currently partitioned across many different services; moreover, it reinforces the fact that it is not possible for users to manually retrieve, store and access their extensive digital data without the support of a personalized information management tool. |