publications([{
   "lang": "en",
   "type_publi": "icolcomlec",
   "doi": "https://doi.org/10.1109/PacificVis.2018.00023",
   "title": "Composite Visual Mapping for Time Series Visualization",
   "url": "http://iihm.imag.fr/jabbari",
   "abstract": "In the information visualization reference model, visual mapping is the most crucial step in producing a visualization from a data set. The conventional visual mapping maps each data attribute onto a single visual channel (e.g. the year of production of a car to the position on the horizontal axis). In this work, we investigate composite visual mapping: mapping single data attributes onto several visual channels, each one representing one aspect of the data attribute (e.g. its order of magnitude, or its trend component). We first propose a table which allows us to explore the design space of composite mappings by offering a systematic overview of channel combinations. We expect that using more than one visual channel for communicating a data attribute increases the bandwidth of information presentation by displaying separable information on different aspects of data. In order to evaluate this point, we compare horizon graph, an existing technique which successfully adopts a composite visual mapping, with a selection of alternative composite mappings. We show that some of those mappings perform as well as –and in some cases even better than– horizon graph in terms of accuracy and speed. Our results confirm that the benefits of composite visual mapping are not limited to horizon graph. We thus recommend the use of composite visual mapping when users are simultaneously interested in several aspects of data attributes.",
   "authors": {
      "1": {
         "first_name": "Ali",
         "last_name": "Jabbari"
      },
      "2": {
         "first_name": "Renaud",
         "last_name": "Blanch"
      },
      "3": {
         "first_name": "Sophie",
         "last_name": "Dupuy-Chessa"
      }
   },
   "year": 2018,
   "uri": "http://iihm.imag.fr/publication/JBD18a/",
   "pages": "116-124",
   "bibtype": "inproceedings",
   "id": 812,
   "abbr": "JBD18a",
   "address": "Kobe, Japon",
   "date": "2018-04-10",
   "document": "http://iihm.imag.fr/publs/2018/PVis2018-Jabbari-CompositeMapping.pdf",
   "type": "Conférences internationales de large diffusion avec comité de lecture sur texte complet",
   "booktitle": "Proceedings of the 11th IEEE Pacific Visualization Symposium (PacificVis 2018)"
},
{
   "lang": "en",
   "type_publi": "colcomlec",
   "doi": "https://doi.org/10.1145/3286689.3286694",
   "title": "Beyond Horizon Graphs : Space Efficient Time Series Visualization with Composite Visual Mapping",
   "url": "https://hal.archives-ouvertes.fr/hal-01899018",
   "abstract": "Restricted screen space is a limit to visualization of time series regardless of the medium. To address this challenge, we introduce new space-efficient visual designs for time series based on an approach similar to the well established Horizon Graph, namely \"\"composite\"\" visual mapping. In this approach, each data attribute is decomposed into two components and then each component is mapped onto a separate visual channel. Our visual designs consist in different combinations of geometric and optical visual channels.\r\n<br />\r\n\r\nWe compare our propositions with Horizon Graph across different chart heights and we measure accuracy and speed of users in a discrimination and estimation task. Our results show that although Horizon Graph perform best at larger chart heights, our propositions demonstrate same levels of accuracy in small chart heights. Moreover, at least one of our propositions has significant advantage over Horizon Graphs in terms of speed. Based on our findings, we propose design guidelines for using composite visual mapping and combinations of optical visual channels in limited vertical screen resolution settings.\r\n",
   "authors": {
      "1": {
         "first_name": "Ali",
         "last_name": "Jabbari"
      },
      "2": {
         "first_name": "Renaud",
         "last_name": "Blanch"
      },
      "3": {
         "first_name": "Sophie",
         "last_name": "Dupuy-Chessa"
      }
   },
   "year": 2018,
   "uri": "http://iihm.imag.fr/publication/JBD18b/",
   "pages": "73-82",
   "bibtype": "inproceedings",
   "id": 832,
   "abbr": "JBD18b",
   "address": "Brest, France",
   "date": "2018-10-23",
   "document": "http://iihm.imag.fr/publs/2018/IHM18-Jabbari-Composite.pdf",
   "type": "Conférences nationales avec comité de lecture sur texte complet",
   "booktitle": "Actes de la 30ème conférence francophone sur l'Interaction Homme-Machine (IHM 2018)"
},
{
   "lang": "en",
   "type_publi": "these",
   "doi": "https://tel.archives-ouvertes.fr/tel-01913579",
   "title": "Composite Visual Mapping for Time Series Visualization",
   "url": "http://iihm.imag.fr/jabbari/",
   "abstract": "Time series are one of the most common types of recorded data in various scientific, industrial, and financial domains. Depending on the context, time series analysis are used for a variety of purposes: forecasting, estimation, classification, and trend and event detection. Thanks to the outstanding capabilities of human visual perception, visualization remains one of the most powerful tools for data analysis, particularly for time series. With the increase in data sets’ volume and complexity, new visualization techniques are clearly needed to improve data analysis. They aim to facilitate visual analysis in specified situations, tasks, or for unguided exploratory analysis.\r\n<br />\r\n\r\nVisualization is based upon visual mapping, which consists in association of data values to visual channels, e.g. position, size, and color of the graphical elements. In this regard, the most familiar form of time series visualization, i.e. line charts, consists in a mapping of data values to the vertical position of the line. However, a single visual mapping is not suitable for all situations and analytical objectives. Our goal is to introduce alternatives to the conventional visual mapping and find situations in which, the new approach compensate for the simplicity and familiarity of the existing techniques. We present a review of the existing literature on time series visualization and then, we focus on the existing approaches to visual mapping.\r\n<br />\r\n\r\nNext, we present our contributions. Our first contribution is a systematic study of a composite visual mapping which consists in using combinations of visual channels to communicate different facets of a time series. By means of several user studies, we compare our new visual mappings with an existing reference technique and we measure users’ speed and accuracy in different analytical tasks. Our results show that the new visual designs lead to analytical performances close to those of the existing techniques without being unnecessarily complex or requiring training. Also, some of the proposed mappings outperform the existing techniques in space constrained situations. Space efficiency is of great importance to simultaneous visualization of large volumes of data or visualization on small screens. Both scenarios are among the current challenges in information visualization.\r\n",
   "year": 2018,
   "uri": "http://iihm.imag.fr/publication/J18a/",
   "id": 833,
   "bibtype": "phdthesis",
   "abbr": "J18a",
   "authors": {
      "1": {
         "first_name": "Ali",
         "last_name": "Jabbari"
      }
   },
   "date": "2018-07-04",
   "document": "http://iihm.imag.fr/publs/2018/these_ali.pdf",
   "type": "Thèses et habilitations",
   "pages": "121"
}]);