Abstract. Advances of sensor and RFID technology provide significant new power for humans to sense, understand and manage the world. RFID provides fast data collection with precise identification of objects with unique IDs without line of sight, thus it can be used for identifying, locating, tracking and monitoring physical objects. Despite these benefits, RFID poses many challenges for data processing and management: i) RFID observations contain duplicates, which have to be filtered; ii) RFID observations have implicit meanings, which have to be transformed and aggregated into semantic data represented in their data models; and iii) RFID data are temporal, streaming, and in high volume, and have to be processed on the fly. Thus, a general RFID data processing framework is needed to automate the transformation of physical RFID observations into the virtual counterparts in the virtual world linked to business applications. In this paper, we take an event-oriented approach to process RFID data, by devising RFID application logic into complex events. We then formalize the specification and semantics of RFID events and rules. We demonstrate that traditional ECA event engine cannot be used to support highly temporally constrained RFID events, and develop an RFID event detection engine that can effectively process complex RFID events. The declarative event-based approach greatly simplifies the work of RFID data processing, and significantly reduces the cost of RFID data integration.
(Steward et al. 1988;Roth et al. 1989;Rushlow et al. 1989;Steward 1989). At high concentrations, Dorsal activates twist and snail in the most ventral region of the embryo, which gives rise to the mesoderm (Jiang et al. 1991;Pan et al. 1991;Thisse et al. 1991;Ip et al. 1992a). Intermediate concentrations of Dorsal allow the expression of rhomboid and singleminded in ventrolateral regions, which become neurogenic ectoderm (Ip et al. 1992b;Kasai et al. 1992). Low concentrations of Dorsal (or no Dorsal) result in the expression of decapentaplegic (dpp), zerknfillt (zen), and tolloid (tld) in dorsal and dorsolateral regions, which dif2Present address:
By providing an integrated and optimized support for user-defined aggregates (UDAs), data stream management systems (DSMS) can achieve superior power and generality while preserving compatibility with current SQL standards. This is demonstrated by the Stream Mill system that, through is Expressive Stream Language (ESL), efficiently supports a wide range of applications-including very advanced ones such as data stream mining, streaming XML processing, time-series queries, and RFID event processing. ESL supports physical and logical windows (with optional slides and tumbles) on both built-in aggregates and UDAs, using a simple framework that applies uniformly to both aggregate functions written in an external procedural languages and those natively written in ESL. The constructs introduced in ESL extend the power and generality of DSMS, and are conducive to UDA-specific optimization and efficient execution as demonstrated by several experiments.
An RFID system consists of RFID readers with antennas, host computers, and transponders or RF tags which are RFID technology provides significant advantages over recognized by the readers. An RFID tag is uniquely identraditional object-tracking technologies and is increasingly tified by a tag ID stored in its memory and can be attached adopted and deployed in real applications. RFID applicato almost anything. Such IDs are specified through the EPC tions generate large volume of streaming data, which have (Electronic Product Code) standard [4]. In addition, RFID to be automatically filtered, processed, and transformed technology can also be used in conjunction with sensors that into semantic data, and integrated into business applicameasure varieties of physical measurements, such as sentions. Indeed, RFID data are highly temporal, and RFID sors for temperature, humidity, blood pressure, etc, which observations form complex temporal event patterns which provide extra information for the entity uniquely identified can be very differentfor various RFID applications. Thus, it by the RFID tag. is desirable to have a general RFID data processing frame-RFID technology makes it possible to i) collect large work with a powerful language, for the end users to examount of data for tracking and identifying physical obpress a variety of queries on RFID data streams, as well jects along their history [19] and ii) real-time monitor physas detecting complex events patterns. While data stream ical objects and their environment [20]. While RFID obsermanagement systems (DSMSs) are emerging for optimized vations are simple primitive events (consisting of reader's stream data processing, they usually lack the language con-EPC code, observed tag ID and the observation timestamp), struct support for temporal event detection. In this paper; RFID observation streams from multiple readers form comwe discuss a stream query language to provide comprehenplex event patterns mostly temporal in nature [19, 20]-sive temporal event detection, through temporal operators to represent business application logic. This poses a sigand extension of sliding-window constructs. With the intenificant challenge for RFID data processing for the followgration of temporal event detection, a DSMS has the capaing requirements of: i) automatically filtering, interpreting bility to serve as a powerful system for RFID data processand transforming raw RFID observation data into semaning. tic business logic data; ii) real-time monitoring and querying physical objects and their environment; iii) the capability to process high volume RFID data streams; and iv)
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